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What is a Concept Paper and How do You Write One?

• By DiscoverPhDs
• August 26, 2020

What is a Concept Paper?

A concept paper is a short document written by a researcher before starting their research project, with the purpose of explaining what the study is about, why it is important and the methods that will be used.

The concept paper will include your proposed research title, a brief introduction to the subject, the aim of the study, the research questions you intend to answer, the type of data you will collect and how you will collect it. A concept paper can also be referred to as a research proposal.

What is the Purpose of a Concept Paper?

The primary aim of a research concept paper is to convince the reader that the proposed research project is worth doing. This means that the reader should first agree that the research study is novel and interesting. They should be convinced that there is a need for this research and that the research aims and questions are appropriate.

Finally, they should be satisfied that the methods for data collection proposed are feasible, are likely to work and can be performed within the specific time period allocated for this project.

The three main scenarios in which you may need to write a concept paper are if you are:

• A final year undergraduate or master’s student preparing to start a research project with a supervisor.
• A student submitting a research proposal to pursue a PhD project under the supervision of a professor.
• A principal investigator submitting a proposal to a funding body to secure financial support for a research project.

How Long is a Concept Paper?

The concept paper format is usually between 2 and 3 pages in length for students writing proposals for undergraduate, master’s or PhD projects. Concept papers written as part of funding applications may be over 20 pages in length.

How do you Write a Concept Paper?

There are 6 important aspects to consider when writing a concept paper or research proposal:

• 1. The wording of the title page, which is best presented as a question for this type of document. At this study concept stage, you can write the title a bit catchier, for example “Are 3D Printed Engine Parts Safe for Use in Aircraft?”.
• A brief introduction and review of relevant existing literature published within the subject area and identification of where the gaps in knowledge are. This last bit is particularly important as it guides you in defining the statement of the problem. The concept paper should provide a succinct summary of ‘the problem’, which is usually related to what is unknown or poorly understood about your research topic . By the end of the concept paper, the reader should be clear on how your research idea will provide a ‘solution’ to this problem.
• The overarching research aim of your proposed study and the objectives and/or questions you will address to achieve this aim. Align all of these with the problem statement; i.e. write each research question as a clear response to addressing the limitations and gaps identified from previous literature. Also give a clear description of your primary hypothesis.
• The specific data outputs that you plan to capture. For example, will this be qualitative or quantitative data? Do you plan to capture data at specific time points or at other defined intervals? Do you need to repeat data capture to asses any repeatability and reproducibility questions?
• The research methodology you will use to capture this data, including any specific measurement or analysis equipment and software you will use, and a consideration of statistical tests to help interpret the data. If your research requires the use of questionnaires, how will these be prepared and validated? In what sort of time frame would you plan to collect this data?
• Finally, include a statement of the significance of the study , explaining why your research is important and impactful. This can be in the form of a concluding paragraph that reiterate the statement of the problem, clarifies how your research will address this and explains who will benefit from your research and how.

You may need to include a short summary of the timeline for completing the research project. Defining milestones of the time points at which you intend to complete certain tasks can help to show that you’ve considered the practicalities of running this study. It also shows that what you have proposed is feasible in order to achieve your research goal.

If you’re pitching your proposed project to a funder, they may allocate a proportion of the money based on the satisfactory outcome of each milestone. These stakeholders may also be motivated by knowing that you intend to convert your dissertation into an article for journal publication; this level of dissemination is of high importance to them.

Additionally, you may be asked to provide a brief summary of the projected costs of running the study. For a PhD project this could be the bench fees associated with consumables and the cost of any travel if required.

Make sure to include references and cite all other literature and previous research that you discuss in your concept paper.

This guide gave you an overview of the key elements you need to know about when writing concept papers. The purpose of these are first to convey to the reader what your project’s purpose is and why your research topic is important; this is based on the development of a problem statement using evidence from your literature review.

Explain how it may positively impact your research field and if your proposed research design is appropriate and your planned research method achievable.

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Concept Papers in Research: Deciphering the blueprint of brilliance

Concept papers hold significant importance as a precursor to a full-fledged research proposal in academia and research. Understanding the nuances and significance of a concept paper is essential for any researcher aiming to lay a strong foundation for their investigation.

Table of Contents

What Is Concept Paper

A concept paper can be defined as a concise document which outlines the fundamental aspects of a grant proposal. It outlines the initial ideas, objectives, and theoretical framework of a proposed research project. It is usually two to three-page long overview of the proposal. However, they differ from both research proposal and original research paper in lacking a detailed plan and methodology for a specific study as in research proposal provides and exclusion of the findings and analysis of a completed research project as in an original research paper. A concept paper primarily focuses on introducing the basic idea, intended research question, and the framework that will guide the research.

Purpose of a Concept Paper

A concept paper serves as an initial document, commonly required by private organizations before a formal proposal submission. It offers a preliminary overview of a project or research’s purpose, method, and implementation. It acts as a roadmap, providing clarity and coherence in research direction. Additionally, it also acts as a tool for receiving informal input. The paper is used for internal decision-making, seeking approval from the board, and securing commitment from partners. It promotes cohesive communication and serves as a professional and respectful tool in collaboration.

These papers aid in focusing on the core objectives, theoretical underpinnings, and potential methodology of the research, enabling researchers to gain initial feedback and refine their ideas before delving into detailed research.

Key Elements of a Concept Paper

Key elements of a concept paper include the title page , background , literature review , problem statement , methodology, timeline, and references. It’s crucial for researchers seeking grants as it helps evaluators assess the relevance and feasibility of the proposed research.

Writing an effective concept paper in academic research involves understanding and incorporating essential elements:

How to Write a Concept Paper?

To ensure an effective concept paper, it’s recommended to select a compelling research topic, pose numerous research questions and incorporate data and numbers to support the project’s rationale. The document must be concise (around five pages) after tailoring the content and following the formatting requirements. Additionally, infographics and scientific illustrations can enhance the document’s impact and engagement with the audience. The steps to write a concept paper are as follows:

1. Write a Crisp Title:

Choose a clear, descriptive title that encapsulates the main idea. The title should express the paper’s content. It should serve as a preview for the reader.

2. Provide a Background Information:

Give a background information about the issue or topic. Define the key terminologies or concepts. Review existing literature to identify the gaps your concept paper aims to fill.

3. Outline Contents in the Introduction:

Introduce the concept paper with a brief overview of the problem or idea you’re addressing. Explain its significance. Identify the specific knowledge gaps your research aims to address and mention any contradictory theories related to your research question.

4. Define a Mission Statement:

The mission statement follows a clear problem statement that defines the problem or concept that need to be addressed. Write a concise mission statement that engages your research purpose and explains why gaining the reader’s approval will benefit your field.

5. Explain the Research Aim and Objectives:

Explain why your research is important and the specific questions you aim to answer through your research. State the specific goals and objectives your concept intends to achieve. Provide a detailed explanation of your concept. What is it, how does it work, and what makes it unique?

6. Detail the Methodology:

Discuss the research methods you plan to use, such as surveys, experiments, case studies, interviews, and observations. Mention any ethical concerns related to your research.

7. Outline Proposed Methods and Potential Impact:

Provide detailed information on how you will conduct your research, including any specialized equipment or collaborations. Discuss the expected results or impacts of implementing the concept. Highlight the potential benefits, whether social, economic, or otherwise.

8. Mention the Feasibility

Discuss the resources necessary for the concept’s execution. Mention the expected duration of the research and specific milestones. Outline a proposed timeline for implementing the concept.

9. Include a Support Section:

Include a section that breaks down the project’s budget, explaining the overall cost and individual expenses to demonstrate how the allocated funds will be used.

10. Provide a Conclusion:

Summarize the key points and restate the importance of the concept. If necessary, include a call to action or next steps.

Although the structure and elements of a concept paper may vary depending on the specific requirements, you can tailor your document based on the guidelines or instructions you’ve been given.

Here are some tips to write a concept paper:

Example of a Concept Paper

Here is an example of a concept paper. Please note, this is a generalized example. Your concept paper should align with the specific requirements, guidelines, and objectives you aim to achieve in your proposal. Tailor it accordingly to the needs and context of the initiative you are proposing.

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Importance of a Concept Paper

Concept papers serve various fields, influencing the direction and potential of research in science, social sciences, technology, and more. They contribute to the formulation of groundbreaking studies and novel ideas that can impact societal, economic, and academic spheres.

A concept paper serves several crucial purposes in various fields:

In summary, a well-crafted concept paper is essential in outlining a clear, concise, and structured framework for new ideas or proposals. It helps in assessing the feasibility, viability, and potential impact of the concept before investing significant resources into its implementation.

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Role of AI in Writing Concept Papers

The increasing use of AI, particularly generative models, has facilitated the writing process for concept papers. Responsible use involves leveraging AI to assist in ideation, organization, and language refinement while ensuring that the originality and ethical standards of research are maintained.

AI plays a significant role in aiding the creation and development of concept papers in several ways:

1. Idea Generation and Organization

AI tools can assist in brainstorming initial ideas for concept papers based on key concepts. They can help in organizing information, creating outlines, and structuring the content effectively.

2. Summarizing Research and Data Analysis

AI-powered tools can assist in conducting comprehensive literature reviews, helping writers to gather and synthesize relevant information. AI algorithms can process and analyze vast amounts of data, providing insights and statistics to support the concept presented in the paper.

3. Language and Style Enhancement

AI grammar checker tools can help writers by offering grammar, style, and tone suggestions, ensuring professionalism. It can also facilitate translation, in case a global collaboration.

4. Collaboration and Feedback

AI platforms offer collaborative features that enable multiple authors to work simultaneously on a concept paper, allowing for real-time contributions and edits.

5. Customization and Personalization

AI algorithms can provide personalized recommendations based on the specific requirements or context of the concept paper. They can assist in tailoring the concept paper according to the target audience or specific guidelines.

6. Automation and Efficiency

AI can automate certain tasks, such as citation formatting, bibliography creation, or reference checking, saving time for the writer.

7. Analytics and Prediction

AI models can predict potential outcomes or impacts based on the information provided, helping writers anticipate the possible consequences of the proposed concept.

8. Real-Time Assistance

AI-driven chat-bots can provide real-time support and answers to specific questions related to the concept paper writing process.

AI’s role in writing concept papers significantly streamlines the writing process, enhances the quality of the content, and provides valuable assistance in various stages of development, contributing to the overall effectiveness of the final document.

Concept papers serve as the stepping stone in the research journey, aiding in the crystallization of ideas and the formulation of robust research proposals. It the cornerstone for translating ideas into impactful realities. Their significance spans diverse domains, from academia to business, enabling stakeholders to evaluate, invest, and realize the potential of groundbreaking concepts.

Frequently Asked Questions

A concept paper can be defined as a concise document outlining the fundamental aspects of a grant proposal such as the initial ideas, objectives, and theoretical framework of a proposed research project.

A good concept paper should offer a clear and comprehensive overview of the proposed research. It should demonstrate a strong understanding of the subject matter and outline a structured plan for its execution.

Concept paper is important to develop and clarify ideas, develop and evaluate proposal, inviting collaboration and collecting feedback, presenting proposals for academic and research initiatives and allocating resources.

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The role of disciplinary perspectives in an epistemology of scientific models

• Paper in Philosophy of Science in Practice
• Open access
• Published: 01 July 2020
• Volume 10 , article number  31 , ( 2020 )

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• Mieke Boon   ORCID: orcid.org/0000-0003-2492-2854 1  

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The purpose of this article is to develop an epistemology of scientific models in scientific research practices, and to show that disciplinary perspectives have crucial role in such an epistemology. A transcendental (Kantian) approach is taken, aimed at explanations of the kinds of questions relevant to the intended epistemology, such as “How is it possible that models provide knowledge about aspects of reality?” The approach is also pragmatic in the sense that the questions and explanations must be adequate and relevant to concrete scientific practice. First it is explained why the idea of models as representations in terms of similarity or isomorphism between a model and its target is too limited as a basis for this epistemology. An important finding is that the target-phenomenon is usually not something that can be observed in a straightforward manner, but requires both characterization in terms of measurable variables and subsumption under (scientific) concepts. The loss of this basis leads to a number of issues, such as: how can models be interpreted as representations if models also include conceptually meaningful linguistic content; how can researchers identify non-observable real-world target-phenomena that are then represented in the model; how do models enable inferential reasoning in performing epistemic tasks by researchers; and, how to justify scientific models. My proposal is to deal with these issues by analyzing how models are constructed, rather than by looking at ready-made models. Based on this analysis, I claim that the identification of phenomena and the construction of scientific models is guided and also confined by the disciplinary perspective within which researchers in a scientific discipline have learned to work. I propose a Kuhnian framework by which the disciplinary perspective can be systematically articulated. Finally, I argue that harmful forms of subjectivism, due to the loss of the belief that models objectively represent aspects of reality, can be overcome by making the disciplinary perspective(s) in a research project explicit, thereby enabling its critical assessment, for which the proposed Kuhnian framework provides a tool.

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1 Introduction

This article aims at developing an epistemology of scientific models. The focus is on empirical and experimental research practices that work in the context of concrete societal or (socio-)technological challenges. Footnote 1 I will argue that the role of disciplinary perspectives is crucial to an adequate epistemology of scientific models in these practices.

Traditional philosophy of science focused of the role of models in testing or justifying abstract theories, which has been systematically worked out in the semantic view of theories. I will explicate this as an epistemology of models, and use this as a point of reference for developing an epistemology of scientific models that does more justice to their roles in scientific research practices. The adjective ‘scientific’ is to emphasize that the intended epistemology is about models that play an independent epistemic role, rather than just serving in the justification of theories. I will argue that an epistemology of scientific models in research practices involves a number of interrelated questions that require philosophical clarification. It begins with the basic question: “What is a scientific model?” In the philosophy of science, a common answer is that a model is a representation of a real-world target-system or phenomenon. Footnote 2 , Footnote 3 This leads to the next question: “What is meant by the idea that models represent a target-phenomenon?” When oriented at scientific practice and the epistemic uses of models, an epistemology of scientific models must also address: “How is it possible that humans gain knowledge about aspects of reality by scientific models?” This points at a more specific question: “How is it possible that scientific models allow for epistemic tasks and inferential reasoning by humans?” Furthermore, assuming that scientific models are used for performing epistemic tasks raises the question: “How are scientific models justified ?” It will appear that also the notion of ‘the target-phenomena represented by the model’ requires attention, in particular when scientific models represent target-phenomena that are not observable in a straight-forward manner. This introduces two additional questions: “What is a phenomenon?” and “How is it possible that models represent non-observable target-phenomena?” Having addressed these questions in the first part of this article, in the second part I will argue that disciplinary perspectives form an inherent part of the proposed epistemology of scientific models. Here, I will propose a Kuhnian framework that enables to systematically articulate and critically evaluate the disciplinary perspective(s) of researchers working in research projects.

In developing an epistemology of scientific models, I adopt a transcendental (Kantian) and pragmatic approach. In this approach the format of asking philosophical questions is: “How is it possible that (for example, models provide knowledge about aspects of reality)?” In other words, “What must be presupposed about scientific practices, the character of epistemic entities (e.g., models), and human cognition to explain that this is possible?” A transcendental approach thus seeks explanations for the “How is it possible …?” questions such as those raised above. Conversely, the “What is …?” questions are mostly secondary in the sense of being based on these explanations. Footnote 4 The pragmatic part of my approach is that the questions and explanations must be adequate and relevant to concrete scientific practice.

An overview of the structure and conclusions of this article can be found in Section 7 .

2 The semantic view: models as representations of theories

2.1 what are models in the semantic view of theories.

This section aims to show that the semantic view offers a straightforward account of the representational relationship between scientific models and real-world target-phenomena, but next, that this account is too limited as an epistemology of scientific models in scientific research practices.

In the semantic view, the role assigned to models is subordinate to the question of how abstract theories can be tested. Testing a theory involves combining a ‘top-down’ and a ‘bottom-up’ approach (see, Figure 1 in Giere 2010 ). Top-down, models of imaginary phenomena or systems (for example the ideal harmonic oscillator) are (mathematically) derived from the abstract theory (e.g., axiomatic systems such as Newton’s laws of motion). Giere calls them representational models . These representational models are sometimes called representations of the theory but are also referred to as instantiations of the theory (also see Giere 1999 , 167–8). Concurrently, these models represent the imaginary phenomenon.

Here I introduce the notion of imaginary phenomena to make a clear distinction with the real-world phenomenon, for example, real-world oscillations. It is worth stressing that the philosophical focus of the semantic view is not primarily on the modeling of a ‘real-world’ phenomena. Nevertheless, the purpose of the semantic view to explain how theories are tested does require to connect between the imagined phenomenon and a real-world target-phenomenon.

The model derived from the abstract theory usually consists of a set of mathematical equations, which can be plotted in graphs by making calculations that predict model-outcomes . Bottom-up, data are generated through real-world experiments, for example, experiments that generate real oscillations and produce data by measuring location or angle as a function of time. On the basis of these experimentally generated data-sets of the real-world phenomenon, so-called data-models are generated by also using theoretical knowledge about the experimental technologies and statistical procedures on ‘raw’ data. Data-models represent the real-world target-phenomenon. These data-models can also be plotted in graphs. Subsequently, the test of the theory consists of comparing (e.g., visually) the plotted (non-linguistic) structures that are based ‘top-down’ on the representational model that represents the imaginary phenomenon under conditions occurring in the experiments, and ‘bottom-up’ on the plotted data-model that represents the real-world phenomenon at the experimental conditions. Footnote 5 Based on this methodology of comparing theoretically predicted and experimentally generated (non-linguistic) structures, scientists decide whether the theory meets epistemic criteria such as empirical adequacy (Van Fraassen 1980 , 2008 ).

It is important to note that the semantic view provides an epistemology of abstract theories that draws on the possibility to objectively compare structures. Apparently, one structure can be called the representation of another structure because the semantic relationship between them is (partially) isomorphic. This approach to testing theories only works when using non-linguistic entities. Footnote 6 My aim is not to claim that the semantic view is philosophically unproblematic, but to emphasize that its plausibility is based on the premise of comparable relationships between structures , which is evaluated in terms of semantic notions such as ‘(partial)-isomorphism,’ ‘similarity,’ ‘resemblance,’ ‘likeness,’ or ‘mapping.’

2.2 How are scientific models justified?

An epistemology of scientific models Footnote 7 requires an explanation of how these models are justified or evaluated, which means that it must be assessed whether they are correct about the real-world target-phenomenon. First, what can we learn from the semantic view about testing scientific models?

The semantic view makes it clear that testing occurs by comparing theoretically generated models (i.e., non-linguistic structures representing the theory) with empirically grounded structures (i.e., non-linguistic structures representing the real-world target-phenomenon). Hence, on the one hand, it is the model-outcomes generated by the non-linguistic structure that is derived from the theory to represent the imaginary phenomenon (e.g., the imaginary harmonic oscillator) at the physical conditions in the experimental set-up, and on the other hand, the data-model generated by an experimental set-up that somehow mimics the imaginary phenomenon. Comparison requires that researchers manage to physically generate the imaginary phenomenon by means of the experimental set-up. Crucially, this involves that the variables that characterize the imaginary phenomenon in the model are the same as the measurable variables in the experimental set-up.

The asset of the semantic view is that comparison between the scientific model and the real-world target-phenomenon merely occurs between (non-linguistic) structures that only make use of the measurable variables (e.g., time, location, angle, length, mass). Hence, the scientific model represents the imagined phenomenon not ‘literally’ as a picture or photograph, but in terms of a set of measurable variables, while the real-world phenomenon is represented in terms of the same set of measurable variables. Moreover, in the case of non-observable phenomena, the comparison is ‘only’ made between model-outcomes and data-models — i.e., between a structure generated by the scientific model at conditions in the experimental set-up, on the one hand, and data-models derived from data measured in an experimental set-up, on the other hand. There is no ‘direct’ comparison between the scientific model and the purported non-observable real-world phenomenon. Footnote 8

3 Models as representations of real-world phenomena

3.1 models as mediators and autonomous agents.

Crucial to the semantic view of theories is the idea that scientific models are (mathematically) derived from abstract scientific theories. In that capacity, scientific models are used to justify or test the abstract theory. In the renowned collection Models as Mediators , Morrison and Morgan (eds. 1999 ) defend a more extended view of models in science. Footnote 9 Their goal is “to clarify at least some of the ways in which models can act as autonomous mediators in the sciences and to uncover the means by which they function as a source of knowledge” (ibid, 8). They introduce the notion of models as mediating instruments , and argue that “if models are to play an autonomous role allowing them to mediate between our theories and the world, and allowing us to learn about one or the other, they require such partial independence” (ibid, 17). Clearly, their ideas are still close to the semantic view when they assume that “models represent either aspects of our theories, or aspects of our world, or more typically aspects of both at once, … [T]he model represents, in its details, both the theory and a real world pendulum” (ibid, 32). At the same time, they depart from the semantic view when claiming that the model “functions as an autonomous instrument which allows us to make the correct calculations for measurements to find out a particular piece of information about the world” (ibid, 32).

Likewise, Morrison ( 1999 ) assumes that models rather than abstract theory represent and explain the behavior of physical systems. She assumes that models are derived from theories, which accords with the semantic view, but she expands on it by defending that models do so in a way that makes them autonomous agents in the production of scientific knowledge. She explains this idea by an example showing that the model of the boundary layer in fluid mechanics cannot be mathematically derived from the Navier-Stokes equations alone, but also involves phenomenological descriptions and conceptual understanding of viscous flows. Thus, Morrison’s analysis shows the crucial role of descriptions and conceptual content in constructing scientific models that cannot be derived from abstract theory only. Therefore, an epistemology of scientific models must take into account the role of linguistic and conceptual content. Yet, this implies that we lose the aforementioned benefits of the semantic view that are based on the assumption that models are non-linguistic entities.

In summary, the idea of models as mediators shows that the semantic view gives a very limited view on the role of models in scientific practice. First, not all models are derived from abstract theories. Secondly, testing abstract theory is not the only epistemic function of models, but also have independent (autonomous) epistemic roles in science. This means that models themselves are sources of knowledge in the sense that models can be used to generate knowledge about the real-world target-phenomenon. Thirdly, models also entail linguistic (conceptually meaningful epistemic) content.

3.2 How do scientific models represent non-observable target-phenomena?

The epistemology of scientific models according to the presented interpretation of the semantic view involves that, in the case of non-observable phenomena, the representational relationship exists only between model-outcomes and data-models . More specifically, there is only a representational relationship between the non-linguistic outcomes of the scientific model calculated at conditions in the experimental set-up, and data-models derived from data measured in an experimental set-up that supposedly generates or investigates the purported non-observable phenomenon. On this account, there is not a ‘direct’ representational relationship in terms of isomorphism or similarity between the scientific model and the purported non-observable phenomenon.

However, current accounts of scientific models do not seem to adopt this very restricted sense in which the notions ‘representation,’ and ‘similarity,’ etc. are to be understood. To the contrary, authors often suggest that models are a more or less literal (although idealized), picture-like representations of (non-observable) real-world phenomena. This is illustrated, for instance, in these quotes by Giere ( 2002 ): “Models are objects that can be used to represent reality by exhibiting a designated similarity to physical objects … My prototype for a model is a standard road map. This is a physical object (usually made of paper) that I would say represents a terrain in virtue of quite specific spatial similarities. I move on to scale models, such as Watson’s original physical model of DNA.” These kinds of metaphors are intelligible when the model represents observable phenomena (e.g., as in graphic art, or in design), or when the representation can be understood as resulting from a specific type of mapping (e.g., 3D to 2D). In these cases, it is possible for knowledgeable researchers to compare the model with the target-phenomenon. But such a comparison is hard to imagine when it comes to non-observable phenomena (e.g., as in the model of DNA). I therefore tend to agree with Van Fraassen ( 2008 ), who argues that if the meaning of representation involves the idea of likeness or similarity , this can only concern observable phenomena (e.g., ibid., 87). Yet, I disagree with Van Fraassen that phenomena are observable by definition. It is common practice in scientific research to refer to all kinds of non-observable phenomena.

An epistemology of scientific models should therefor include a comprehensible explanation of the representational relationship between scientific models and non-observable real-world target-phenomena. This forces us to specify what we mean by phenomena . In particular, how do we identify and specify the target-phenomenon represented in the model? Is the target-phenomenon represented in the model epistemically independent of the model? How does the target-phenomena become known to us? In the case on non-observable phenomena, does this not already involve a scientific model of the phenomenon? All this implies that an epistemology of scientific models also requires an epistemology of phenomena .

4 An epistemology of observable and unobservable target-phenomena

4.1 what is a phenomenon.

Although there is an extensive literature on phenomena, the question “what is a phenomenon?” is not often discussed in the literature on models. Footnote 10 Therefore, the way in which the concept of phenomenon is used needs clarification. Let us first look at some examples. Well-known historical examples of observable phenomena discovered by scientists are: the orbit of the Moon, the patterns formed in iron filings on paper covering a magnet, the piezo-electric effect, and the emission and absorption spectra in heated hydrogen gas. Commonly, the observed phenomena are represented by drawings or tables or graphs. Footnote 11 In the semantic view, these types of representations are called data or data-models. However, this suggests that the phenomena just described can be reduced to a pattern in data. It suggests that ‘observed phenomena’ are the observed patterns (or structures) that occur in nature or are generated in experimental setups. But in this manner, we seem to lose essential information. Surely, this account of observable phenomena is adequate to the project of the semantic view. But it does not fully cover the roles of phenomena and their modelling in concrete scientific practices. So, can we come up with an account that is more adequate about these roles?

In this section, I aim for an epistemology of phenomena that is adequate with regard to experimental research and scientific modelling practices. First I will analyze various ideas within the philosophy of science about phenomena. Footnote 12 Next, I will outline an account that I find plausible for solving the various conceptual and epistemological issues that emerge in this analysis. Finally, I will explain in Section 5 how this account of phenomena fits with the ideas proposed in this article about how models are constructed within disciplinary perspectives.

4.2 What are phenomena in the semantic view?

I take Van Fraassen as a representative of the interpretation of the semantic view that I endorse. In accordance with his empiricist stance, he assumes that the task of science is to represent the observable phenomena , which he refers to as ‘to save the phenomena’ (Van Fraassen 2008 , 86). This take on the matter implies that not the scientific model, but the model-outcome represents the observable phenomenon. As said, the model-outcome is expected to be (partially) isomorphic to the observed phenomenon at conditions in the experimental setup. It is in this very manner that the model, when correct, represents the observed phenomenon. Clearly, this agrees with how the semantic view explains representational relationships in testing theories. As outline above, observed phenomena in turn, are mere patterns or structures in measured data, which are represented by drawings or tables or graphs, and called data or data-models.

But what does this mean for the content of the scientific model? According to Van Fraassen, “A model often contains much that does not correspond to any observable feature in the domain. Then, from an empiricist point of view, the model’s structure must be taken to reveal structure in the observable phenomena , while the rest of the model must be serving that purpose indirectly” (ibid, 87, my emphasis). In my view, Van Fraassen’s (anti-realist) empiricist position correctly avoids the suggestion that models can provide literal , picture-like representations of unobservable phenomena. Yet, his view also seems quite empty as to the content of the model. In short, in this view on phenomena, the expression “models represent their target-phenomenon” only means that model-outcomes represent observable phenomena, while the content of the model does not represent anything real. Altogether, this view is insufficient regarding the role of non-observable real-world phenomena in scientific practices.

4.3 Van Fraassen on observable phenomena

Van Fraassen’s reason to distinguish between observable and non-observable phenomena is an empiricism that “involves a common sense realism in which reference to observable phenomena is unproblematic” ( 2008 , 3). Although I am sympathetic to Van Fraassen’s empirical stance, his assumption that “the aim of science is to provide empirically adequate theories about what the world is like [i.e., about observable phenomena]”(ibid, 87), is too limited as an account of scientific practices. I agree that observable phenomena play a crucial role in testing empirical adequacy as a way to justify a theory or scientific model. Yet, the aim of science is not only empirically adequate theories, but also the construction of scientific models that allow different kinds of inferential reasoning. Footnote 13

In summary, unlike Van Fraassen’s point of view, I defend that philosophical accounts of models should not be limited to observable phenomena, but also explain the role of postulated non-observable target-phenomena. In my view, non-observable phenomena are postulated to enable different kinds of inferential reasoning, which do not necessarily have to lead to true results, but which are productive to advance scientific research. Footnote 14

4.4 The problem of unobservable phenomena

The other extreme is to assume that a scientific model literally represents an unobservable target-phenomenon. I call this a picture-metaphor of models . A ‘literal representation’ in this context means something akin to how non-linguistic entities such as photographs, drawings, numerical tables, and graphs represent observable phenomena. As outlined above, authors like Giere suggest that these non-linguistic entities are ‘direct’ representations in the sense that humans are capable to recognize them as representations in a straightforward manner. Therefore, the expression “models represent their target-phenomenon” in this interpretation means that scientific models represent non-observable phenomena in a more or less literal fashion. Suggestive examples are the model of DNA referred to by Giere, but also the Bohr model of the atom, and models representing proteins and biochemical mechanisms. I stress that, in view of an epistemology of models, this interpretation is highly problematic because it is unclear how to arrive at more or less literal representations of these kinds of unobservable phenomena.

4.5 Bogen and Woodward’s distinction between data and phenomena

Bogen and Woodward’s ( 1988 ) defense of the notion of phenomena can be taken as a realist, practice oriented response to Van Fraassen’s ( 1980 ) anti-realist notion of “saving the phenomena.” They defend that phenomena are: distinct from data; objective, stable features of the world; not always observable; not low-level theories; and, inferred from data (also see Bogen 2011 ; Woodward 2011 ). Loosely speaking, according to them, data are the observations reported by experimental scientists, while phenomena are objective, stable features of the world to which scientists infer based on reliable data. Data are ‘directly observed’ and idiosyncratic to particular experimental contexts, whereas phenomena have stable, repeatable characteristics that are detectable by means of a variety of different procedures, which may yield quite different kinds of data. Ontologically and epistemologically, they think of phenomena as being in the world, not just the way we talk about or conceptualize the natural order. On their realist account, therefore, phenomena are physical entities that exist independent of us.

Although Bogen & Woodward’s account of phenomena is intuitively plausible from a scientific practice point of view, the philosophical difficulty is that their empiricism requires reconciling two assumptions, namely, on the one hand, that phenomena are inferred from data, and, on the other hand, that phenomena are not some kind of low-level theories. However, inferring phenomena from data, for instance by means of statistical methods, implies that the phenomenon is identical with the data-model derived from the data (Glymour 2002 ), which does not add much to data-models in the semantic view. The other option is that the phenomenon (or should we rather say, the description of the phenomenon) represents a theoretical or conceptual interpretation of the data, which means that conceptual content has been added so that the data (or data-model) is converted into a low-level theory, which they explicitly aim to avoid. Below, I propose that Massimi’s Kantian take on this issue provides a viable alternative.

4.6 Phenomena as entities that ask for scientific explanations

According to Hacking ( 1983 ), “[Phenomenon] has a fairly definite sense in the writing of scientists. A phenomenon is noteworthy . A phenomenon is discernible . A phenomenon is commonly an event or process of a certain type that occurs regularly under definite circumstances” (ibid, 221). Hacking refers to observable phenomena as not only occurrences observed in nature but also new phenomena that are generated in experimental set-ups, often through the operation of new technological instruments and noticed by attentive researchers who intervene with this equipment. I agree with Hacking ( 1983 ) that science postulates successful non-observable phenomena that are held causally responsible for specific observable phenomena and which he calls ‘theoretical entities.’ However, I am hesitant about his ‘entity realism,’ which is why I stick to the expression ‘purported non-observable real-world phenomena.’ Footnote 15

Accordingly, another option to explain what we mean by ‘phenomena’ is that scientific models structure or explain rather than ‘literally’ represent the observed or unobservable phenomenon. Hence, the scientific model can represent the phenomenon in terms of a (logical, morphological, or mathematical) structure that tells more than the data or data-pattern observed in nature or in the laboratory. In addition, a scientific model can also represent an explanation of the observed phenomenon, which usually includes representations of the causal workings in terms of (purported) non-observable real-world phenomena (such as mechanisms). The expression “models represent their target-phenomenon” then means that scientific models represent (purported) ‘underlying’ structures , that are presupposed to explain the observable phenomena – rather than representations being mere ‘ descriptions .’ Altogether, these different options show that it is not fully clear what we actually mean by the expression that “models represent their target-phenomenon.”

4.7 Massimi: phenomena are not ready-made

One of the reasons to argue for the role of disciplinary perspectives in science is that phenomena, be it observable or non-observable, are never observed in a straightforward manner, but always involve what can be called a perspective (e.g., a mathematical or conceptual framework). To make this point, I turn to Massimi’s ( 2007 , 2008 , 2011 ) Kantian account of phenomena. Massimi ( 2008 ) engages in the controversy between scientific realists (including Hacking and Giere) and Van Fraassen’s anti-realist constructive empiricism by asking, “how do we know that the entities, their properties and relations as described by our best scientific theories truly correspond to the way things are in nature?” (ibid, 1). Massimi argues that a prevailing conception of phenomena, according to which phenomena are what ‘appears’ to us and to our perceptual apparatus, is at the heart of this debate. Therefore, both scientific realist and constructive empiricist positions are entrapped in “the view that phenomena are empirical manifestations of what there is” (ibid, 3). This view entails the idea that phenomena are ‘ready-made,’ that is, phenomena lay bare in front of us. Massimi believes this view is inadequate. Her purpose is to show that phenomena are not ready-made for a scientific theory to either save them, as Van Fraassen thinks, or give a literally true story of them, as believed by scientific realists.

The alternative philosophical conception of phenomena she proposes goes back to Kant. Massimi argues that from a Kantian point of view phenomena are not ready-made, not mere empirical manifestations of what there is. Instead, phenomena conform to our ways of representing: “our representation of things as they are given to us does not conform to these things as they are in themselves but rather these objects as appearances conform to our way of representing ” (ibid, 9, my emphases). Therefore, “[A] phenomenon, …, is a conceptually determined appearance . ... phenomena are appearances brought under the concepts of the faculty of understanding so as to make experience finally possible,” (ibid, 10, 11). Massimi makes this idea about phenomena more concrete as follows: “in nature we may observe objects moving in space and time, changing physical state (from solid to liquid to gaseous) or displaying some properties (e.g. being elastic). But these are only appearances [Erscheinungen]. Only when we introduce moving forces as the underlying causes that make the objects move in space, or change their physical state, or displaying some physical or chemical properties, do we have a conceptually determined appearance or phenomenon as the proper object of scientific knowledge” (ibid, 14).

According to Massimi, the crucial, distinctively new feature that Kant introduced in the conception of phenomena is “that a physical phenomenon —intended as a conceptually determined appearance— has built in it from the very outset the concept of a moving force as the cause of the observed appearance. It is the causal concept of a moving force that distinguishes phenomena from appearances, or better, that transforms appearances into phenomena, i.e., objects of possible experience into objects of experience ” (ibid, 14). I agree with Massimi that a Kantian conception of phenomena does better justice to the complexity of phenomena in scientific practice – in particular, by stressing that (representations of) phenomena entail conceptual content introduced by how humans (cognitive agents) conceptualize perceptions and experiences. But new issues emerge that I will address shortly.

4.8 What does ‘representation of a phenomenon’ mean?

Based on this brief overview of ideas about phenomena, another problem emerges, because it has become unclear whether the word ‘phenomenon’ refers to a real-world thing, say a physical phenomenon ‘out there,’ or instead, to something that might just as well be called a representation. The notions of observable and non-observable phenomena in philosophical literature on scientific models suggest that phenomena are real physical things that we can look at or point to. But Massimi’s Kantian account of phenomena assumes that phenomena are conceptually determined appearances. This seems to suggest an idealism that I would rather avoid here. Therefore, I propose firstly, that we must introduce the concept of representations of phenomena to give phenomena a proper place between data and models or theories, and also, to make the phenomenon suitable for epistemic activities such as modelling them. Secondly, close to Massimi, my view is that the representation of a phenomenon involves data being transformed into epistemic entities through mathematical, theoretical and conceptual frameworks.

The progress made in this way is that our representations of phenomena can no longer be understood as if phenomena are somehow ‘literally’ read off or depicted from reality, but must be understood as representations that are the result of an interplay between our experiences, observations and data (the ‘appearances’), on the one hand, and mathematical, theoretical and conceptual frameworks on the other. In Section 6 it will be worked out somewhat further that the role of these frameworks must be interpreted as perspectives.

As a consequence, notions such as ‘descriptions of (unobservable) phenomena’ and ‘models as representations of (unobservable) phenomena’ cannot be intuitively grasped by picture-like views of phenomena. The account of phenomena presented here shows that mathematical, theoretical and conceptual content is built in representations of phenomena. Subsequently, it is precisely this content that enables forms of inferential reasoning by scientific researchers that go beyond mere deductive or inductive forms of reasoning. In this way, we obtain a more adequate account of the role of phenomena in scientific practices. Footnote 16

The conceptual distinction between phenomena and models has become blurred due to the distinction that must be made between the target-phenomenon ‘that exist out there’ and appear to us on the one hand, and ‘descriptions or representations of phenomena’ that count as epistemic entities, on the other. In the account proposed here, the latter should be understood as a ‘conceptually determined appearance’ rather than a ‘literal image of the appearance.’ Consequently, the ‘description or representation of a target-phenomenon’ is already a scientific model 1 of the phenomenon. Next, this ‘description or representation of the target-phenomenon’ (i.e., scientific model 1 ) may ask for an explanation. This will produce yet another scientific model 2 of the same target-phenomenon ‘out there.’ A more precise phrasing of the latter would say that ‘the scientific model 2 represents an explanation of the target-phenomenon.’ Footnote 17 , Footnote 18

5 The construction of models as part of an epistemology of scientific models

5.1 philosophical issues in an epistemology of scientific models.

The discussion so far makes plausible that an epistemology of scientific models must address a number of philosophical issues that arise when we let go of the picture-metaphor of models to explain how scientific models represent non-observable target-phenomena. These concern respectively: (a) Alternatives to the picture-metaphor of scientific models; (b) The assumption that scientific models are mere non-linguistic entities; (c) The question what a real-world target-phenomenon is; (d) The issue of representing non-observable real-world phenomena; (e) The justification of models; (f) The question how models allow for inferential reasoning about the real-world target-phenomenon. These issues seem non-existent as long as the picture-metaphor of models is maintained. However, this metaphor, in which the model is a more or less literal picture of the real-world target-phenomenon, appears problematic as the intuitive basis for our understanding of scientific models. This implies that an epistemology of models must provide an alternative to ‘how models represent’ in a way that satisfactorily resolves these issues. Here, I propose an alternative that does not take the ready-made model and target-phenomenon as the point of departure, but starts from the question of how scientific models are constructed – as this should clarify the questions raised in the transcendental and pragmatic approach aimed at an epistemology of models that suits scientific practice, such as: What are scientific models? What do scientific models represent exactly? How are scientific models connected to the real world? How is it possible that scientific models enable epistemic tasks related to real-world target-phenomena?

Below, the implications of taking the model construction into account in an epistemology of scientific models will be discussed along the lines of the issues (a-f). To this end, I will use a systematic account of the (re)construction of models as proposed by Boon and Knuuttila ( 2009 ) and Knuuttila and Boon ( 2011 ), which has been elaborated into a concrete, practically useful method, called the B&K method for the (re)construction of scientific models in scientific practices (Boon 2020 ). Footnote 19

5.2 Issue a: Alternatives to the picture-metaphor of scientific models – Models as hubs

By starting with the question of how models are constructed, it becomes clear that models are rather a kind of hubs where heterogeneous aspects are brought together and merged into a coherent whole (cf. Nersessian and Patton 2009 ; Nersessian 2009a , b ), which is then called the scientific model of a target-phenomenon. This alternative take on models shows that such an intricate construction process in which different types of content are chosen and merged, most probably does not result into a more or less literal picture of the target-phenomenon. An account of models as constructed epistemic entities (rather than being logically or algorithmically deduced from abstract theories, as in the semantic view), therefore, overcomes the idea that scientific models, metaphorically speaking, are more or less literal (in the sense of structurally similar) pictures of their target.

5.3 Issue b: Are models mere non-linguistic entities?

A widespread, although somewhat implicit, assumption is that models are non-linguistic entities , which may be a relic of the semantic view. In Section 2 it was explained that the advantage of this assumption is that semantic notions such as isomorphism or similarity can account for the representational relationship between observable structures or patterns. However, as was argued in Section 3 , the idea of scientific models as mere non-linguistic entities is too limited. When we consider how actual scientific models of real-world phenomena are constructed, it is clear that models cannot be derived from abstract theories alone, nor can they be generated by a direct picture of reality. Instead, modeling is a construction process in which heterogeneous content is collected and combined into a coherent whole. When we look at the aspects that are built into the model (as in note 19) and think of models that are presented in the scientific literature, it becomes obvious that models are rather a kind of story about the phenomenon. In scientific articles, this story is usually clarified with pictures, diagrams and graphs. But the idea that these non-linguistic elements are ‘the model’ is mistaken. We should rather adopt the idea that the model consists of the entire content of a scientific article.

5.4 Issue c: What is a real-world target-phenomenon?

The question of characterizing the target-phenomenon represented in the model is usually not addressed in the literature of models. Footnote 20 Knuuttila and Boon ( 2011 ), on the other hand, claim that developing a conception of the real-world target-phenomenon is an inherent part of the modelling process (note 19, aspect 2). Therefore, the ‘description’ of the target-phenomenon, and the scientific model of the target-phenomenon are co-constructed epistemic entities. Footnote 21 An example is how Sadi Carnot constructed the model of the ideal heat engine. Footnote 22 By abstracting from much of what seems to characterize real-world heat-engines, Carnot conceives of heat-engines in terms of a phenomenon described as ‘heat is converted into motive-power.’ This is the imagined phenomenon for which he then constructs the model. In this way, imagining (i.e., constructing a ‘description’ of) the target-phenomenon is part of the modelling process. Footnote 23

In line with Massimi’s ( 2008 ) Kantian account of phenomena discussed above (Section 4 ), this example shows that the description of the target-phenomenon is not ‘ready-made.’ It is not an empirical manifestation of what there is, but instead, an interpretation of an observed or experienced object, property or process such as the real heat engine as experienced and interpreted by Carnot. Such an interpretation requires to bring observations, experiences, or data under (scientific) concepts (i.e., ‘subsumption under concepts’). Footnote 24 This can be concepts that already exist, or newly invented scientific concepts (Feest 2010 ; Boon 2012 ). Crucially, in this way conceptual content enters into the model .

Additionally, the way in which target-phenomena are conceived in scientific practice involves the kind of practical and/or scientific problem that the researcher aims to tackle (note 19, aspect 1). In the example of Carnot this implies that his conception Footnote 25 of the phenomenon (the imagined phenomenon) not only encompasses abstract or theoretical concepts such as ‘heat,’ ‘motive power,’ and ‘conversion,’ but also practical or functional concepts concerning the practical problem he aims to solve by his theoretical approach. Footnote 26

In scientific practice, therefore, determining and characterizing the real-world target-phenomenon already involves theoretical and conceptual interpretations of data and experiences as an inherent part of the model construction. As a consequence, the ‘description’ of the real-world target-phenomenon is the imaginary phenomenon. Furthermore, the target-phenomenon is detected (in the real world) by the same measurable variables that characterize the imaginary phenomenon (in the model), securing a representational relationship between a structure generated by the model and a structure generated by the real-world target-system (as in the semantic view).

5.5 Issue d: How do models represent non-observable real-world target-phenomena?

Characterizing the semantic and epistemic relationship between the model and the target-phenomenon in terms of similarity, likeness, etc., makes sense in a picture-metaphor of representation. However, when the representational relationship concerns purported non-observable phenomena it is difficult to understand what ‘similarity’ means, because a direct visual comparison no longer seems to have a clear meaning. Alternatively, the possibility to connect (i.e., to draw a cogent semantic relationship) between the model and the real-world is warranted by how the model is constructed . In particular, when researchers choose the variables that characterize or causally affect the (observable, or the purported non-observable) target-phenomenon, they are guided by which variables are detectable or measurable (see note 19, aspect 6). In other words, these variables are not randomly chosen, nor do they emerge from nowhere. Note that this emphasis on the role of measurable data in constructing scientific models of real-world phenomena complies with how connections between theory and world are established according to the semantic view (Section 2 ). Thus, by including the relevant measurable (physical-technological) variables in the construction of a scientific model, a connection is warranted between the variables that characterize the imaginary phenomenon in the model and those that characterize the real-world target-phenomenon (also see issue c).

5.6 Issue e: The justification of models

The question of how models are justified is not generally dealt with in the literature on models. This may be due to the idea that similarity between model and target-phenomenon suffices as justification: either objectively determined by the comparison between the structure of the model and that of the target, as in the semantic view, or subjectively determined by researchers, as in the accounts by Giere and Suárez. Instead, Knuuttila and Boon ( 2011 ) emphasize that models are partly justified by how the model is built . This involves a creative process of critically searching, selecting, conceptualizing, assessing and combining supposedly adequate and relevant heterogeneous aspects that are forged into a coherent whole (note 19). One of these aspects is the choice of relevant and measurable variables to characterize both the imagined phenomenon represented in the model and the purported real-world phenomena. Another one is the choice of scientific concepts to characterize the phenomenon.

Scientific models are justified and tested in at least three ways that complement each other, namely: (i) by justifying the relevance, physical plausibility, and adequacy of aspects that are built into the model; (ii) by assessing whether the model meets relevant epistemic and pragmatic criteria such as internal coherence, internal consistence, intelligibility and physical plausibility, Footnote 27 and adequacy with regard to the current state of knowledge; and (iii) through empirical or experimental testing against reality by comparing model-outcomes and experimental results in order to achieve empirical adequacy. Footnote 28

In the second part of this article (Section 6 ), I will argue that the choices of the heterogeneous aspects are also guided and justified by the disciplinary perspective within which the researcher works.

5.7 Issue f: How do scientific models allow for inferential reasoning?

An important aspect of an epistemology of scientific models is to explain how models can be sources of (new) knowledge. To be useful for researchers in performing epistemic tasks, scientific models must enable inferential reasoning , either internal to the model in regard of the imaginary object represented in the model (as is emphasized in fictional accounts of models, e.g., Barberousse and Ludwig 2008 ; Suárez 2009a , b ; Contessa 2010 , Toon 2012 ), or externally oriented to generate model-outcomes that describe or represent aspects of the real-world target-phenomenon.

Suárez ( 2003 , 2004 ) proposes an inferential conception of representation , which entails the idea that “[the internal structure of the representation] A allows competent and informed agents to [correctly] draw specific inferences regarding [the target] B” (Suárez 2004 , 773). This does not require “that A [merely] allows deductive reasoning and inference; any type of reasoning —inductive, analogical, abductive— is in principle allowed, and A may be anything as long as it is the vehicle of the reasoning that leads an agent to draw inferences regarding B” (ibid, 773). Furthermore, Suárez stresses that ‘ correctly drawing inferences’ is not equivalent to ‘drawing inferences to true conclusions.’ I adopt Suárez’s idea that scientific models allow inferential reasoning by researchers, and also, that inferential reasoning can be any type of cogent reasoning. However, unlike Suárez’s deflationary notion of representation, I argue that an epistemology of scientific models must also explain how it is possible that models enable inferential reasoning, 13 not only internally, but also related to (purported non-observable) real-world target-phenomena. Footnote 29 , Footnote 30 First of all, as scientific models are not, or not exclusively derived from abstract theories, it must be explained how models are constructed and justified that make these kinds of epistemic activities possible at all, for which the B&K method is proposed (note 19). Next, based on the analysis so far, I assume that: (i) the full content of the model —consisting of the ‘coherent story’ constructed by integrating the aspects put forward in the B&K method (note 19)— allows for such reasoning; (ii) therefore, not only the (non-linguistic) internal structure of scientific models allows for different types of inferential reasoning about the target-phenomenon, but also the conceptual content and other aspects built into the model, such as knowledge regarding the physico-technological interactions with the (supposed) real-world target-phenomenon and theoretical knowledge; and (iii) as has been argued in Section 2 , ‘direct’ comparison between model (model-outcomes) and an unobservable target-phenomenon is (only) possible by means of the measurable or detectable features by which the phenomenon is characterized (also see issue e). In the second part of this article (Section 6 ), I will argue that the disciplinary perspective is the broader framework through which the construction, justification and reasoning with models is possible.

5.8 Taking stock: Towards an epistemology of scientific models

In the first part of this article, I have argued that it needs to be clarified what exactly we mean when we talk about scientific models that supposedly represent non-observable phenomena. Getting a philosophical grip on this is relevant for better understanding scientific practices. The intuitively plausible idea about scientific representation, expressed in sentences such as “[S]cience provides us with representations of atoms, …, and the world’s climate” (Frigg and Nguyen 2016a ), and, “scientific models represent their target-phenomenon,” is problematic. On the basis of my analysis so far an epistemology of scientific models that suits scientific practices can be summarized by the following statements:

An epistemology of scientific models should not be guided by a picture-metaphor but take into account how scientific models are constructed.

Scientific models are constructed by combining heterogeneous aspects (as in note 19), which researchers must integrate into a coherent whole that allows different kinds of inferential reasoning. Additionally, in the process of developing them, models are a kind of hub where these heterogeneous aspects are brought together.

In order to be meaningful and intelligible for scientific researchers, scientific models must also contain linguistic (i.e., conceptually meaningful epistemic) content. This implies that scientific models tell a kind of story rather than being self-explanatory pictures. For that reason, it is better to assume that the full content of a scientific article about a specific target-phenomenon is the scientific model. Indeed, the story told in scientific articles is clarified by means of mathematical formula, graphs, diagrams and pictures, but the idea that these non-linguistic elements are the model is mistaken.

Constructing a model of a target-phenomenon requires a representation (or ‘description’) of the real-world target-phenomenon to begin with. This representation is not ‘ready-made’ but is also the result of creative and constructive activities by researchers. As a consequence, the conceptual articulation of a target-phenomenon is usually part of the modelling process. Footnote 31

Scientific models are tested and justified in at least three different ways that complement each other.

Scientific models enable inferential reasoning (and more broadly, epistemic activities) through the entire content built into the model, which encompasses the (non-linguistic) internal structure, the conceptual content (e.g., scientific concepts), the physico-technological context, theoretical knowledge, etc. (see note 19).

Scientific models are constructed within a specific scientific discipline. Within this discipline, epistemic activities, such as conceptualizing target-phenomena and constructing scientific models, are guided and constrained by a disciplinary perspective . This is the topic of the second part of this article (Section 6 ).

6 Disciplinary perspectives in the construction of scientific models

6.1 disciplinary perspectives in science.

My philosophical argument for defending the indispensable role of disciplinary perspectives in science can be summarized as follows. First, the finding that a direct ‘picture-like’ representational relationship between scientific models and target-phenomena is problematic for philosophical reasons, leads to the idea that this representational relationship must be clarified by investigating how scientific models are constructed. Subsequently, I argued that scientific models in scientific practices are not mere non-linguistic entities (e.g., observed patterns, or mathematical structures derived from abstract theory), but are better understood as hubs in which heterogeneous aspects are combined into a coherent whole that consists of both linguistic and non-linguistic content. Also, the construction of models (as well as the conceptual articulation of the phenomena) requires creative and constructive epistemic activities by researchers, who search, assess, choose and integrate the heterogeneous aspects that they built into the model. Footnote 32 This involves epistemic activities such as, to select, organize, structure and interpret empirical data or phenomena by choosing (or inventing) relevant epistemic components. Examples are: fundamental principles; mathematical structures; physically meaningful concepts (e.g., elasticity, oscillation, force); practical and theoretical knowledge (e.g., abstract theory; phenomenological and scientific laws); and, explanatory hypotheses. In turn, epistemic activities are guided by epistemic and pragmatic criteria such as, coherence, consistency, adequacy, intelligibility, physical plausibility and relevance. Finally, I suggest that the epistemic activities, as well as the choices and judgments that are needed in the construction of scientific models are guided and enabled by the disciplinary perspectives of the practice within which the researcher works.

6.2 The roles of scientific and instrumental perspectives in scientific practices

Metaphorically, perspectives are like a pair of glasses. They enable to see aspects of the world in a specific way. The strong version, which I endorse, holds that without perspectives nothing meaningful is seen or known. Perspectives in this strong version are more like a pair of eyes. Perspectives are therefore not representations, but the means by which representations are generated. Giere ( 2006 ) and Van Fraassen ( 2008 ) have argued that different types of perspectives feature in science, enabling different kinds of both instrumental (technological) and epistemic activities to generate representations. Based on their insights I distinguish different kinds of perspectives in scientific practices:

Firstly, it is only through scientific perspectives in the sense of mathematical and/or conceptual frameworks (including laws, scientific concepts and phenomenological analogies) and fundamental (ontological) beliefs that researchers ‘recognize’ a real-world target-phenomenon in ‘raw’ observations, experiences and data. It is only possible, therefore, to form a mathematically or conceptually meaningful representation of a phenomenon out of ‘raw’ data by means of these kinds of perspectives. Without them, no phenomenon is seen or recognized (also see Rueger 2005 ).

Secondly, researchers generate ‘raw’ data using (technological) instruments and experimental procedures, which therefore count as instrumental perspectives (Giere 2006 ; Van Fraassen 2008 ). Based on the analysis of the semantic view in Section 2 , I add that the data is therefore of a specific type, namely, as determined by the instrumental perspective. Thus, the instrumental perspective determines the specific way in which the purported phenomenon is characterized and represented. For example, the behavior of a bullet is conceptualized as a trajectory, and represented by the measured variable, i.e., the x-y-z location as a function of time, and not the mass, shape or roughness. Footnote 33

Thirdly, researchers use practical and theoretical knowledge about the instruments and procedures indicative of a purported non-observable real-world phenomenon to predict the occurrence of this phenomenon elsewhere (cf., Boon 2017 ). The crux is that although researchers do not have direct access, they suspect that a non-observable phenomenon must be present in the case of physical-technological conditions at which the (non-observable) phenomenon is believed to exist. This type of knowledge about instruments and procedures therefore offers a scientific and instrumental perspective on physical or technological systems, with which researchers can produce knowledge about already known but unobservable aspects of that system.

Fourth, similar to the conceptual articulation of phenomena, the construction of scientific models requires that researchers use theoretical, mathematical and conceptual frameworks to structure, interpret or explain the real-world target-phenomenon (note 19, aspects 8 and 9). In this way, structure and conceptually meaningful content is built into the model, which is crucial for becoming a representation at all. Giere ( 2006 ) argues that the used frameworks are scientific perspectives.

Fifth, epistemic entities such as ‘representations of phenomena’ and ‘scientific models’ can themselves acquire the role of scientific perspectives for performing epistemic tasks. Scientific models, for example, allow different kinds of inferential reasoning , such as deductive, inductive, predictive, explanatory, explorative, hypothetical and ‘what if …’ reasoning about the target-phenomena. In this capacity, the model functions as a perspective with which new hypotheses, knowledge or questions regarding the target-phenomenon are generated. Footnote 34 In addition, scientific models as perspectives also make inferential reasoning possible about interventions with physical or technological systems. For example about how you could manipulate, control, generate or create the (purported) real-world target-phenomenon.

This list shows different types of perspectives and their roles in scientific practices. Below, I will propose a more systematic (Kuhnian) framework to characterize disciplinary perspectives .

6.3 The role of disciplinary perspectives in constructing a phenomenon

The construction of (‘descriptions’ or representations of) target-phenomena takes place in a broader disciplinary context. I propose to call the intellectual, epistemic and conceptual part of the discipline the disciplinary perspective of a scientific practice. Briefly said, the disciplinary perspective enables researchers in a specific scientific discipline to reason and conceptualize, but it also constrains them. Therefore, ‘conceiving of’ (or, ‘conceptually articulating’) the target-phenomenon does not take place in void, but already involves many different aspects that are known to, and understood by the researcher. More specifically, the imagined phenomenon is embedded in a network consisting of different types of intellectual, epistemic and conceptual aspects. Examples are: partially interpreted (i.e., conceptualized) real-world experiences (e.g., the workings of heat, motive-power and heat-engines); scientific concepts and conceptual frameworks (e.g., ‘caloric,’ ‘heat,’ ‘motive-power,’ etc.); established practical knowledge (e.g., about the workings of experimental set-ups); empirical and phenomenological knowledge (e.g., about experimental findings by other scientists); fundamental principles (e.g., heat cannot flow from cold to hot); theoretical knowledge (e.g., the gas-laws of Boyle and others); measurable variables (e.g., mass, volume, pressure, and temperature); and, established conceptually meaningful parameters (e.g., ‘density,’ ‘specific heat’). Footnote 35 A closer look at these aspects makes it clear that this network connects the imagined phenomenon with both the real-world target-phenomenon (e.g., in terms of measurable variables) and with mathematical and conceptually meaningful content (e.g., through theories and concepts). Footnote 36

The network of aspects through which a phenomenon is identified is an important part of the disciplinary perspective that guides and also constrains (communities of) researchers. It enables them to perform creative, critical and constructive epistemic activities in the construction of representations and in the use of models for inferential reasoning. Accordingly, the question of how the representational relationship between the imagined and the purported real-world target-phenomenon is to be understood is explained by how they are embedded in the disciplinary perspective. On the one hand, the imagined phenomenon in the model is conceptualized within the disciplinary perspective. On the other hand, the real-world phenomenon is characterized or determined by experimental setups or (technological) measurement procedures typical of the discipline, and accordingly represented in a specific way through the disciplinary perspective. More specifically, the specific constellation that characterizes or determines the purported non-observable real-world phenomenon includes reference to relevant measurable variables and parameters, measuring instruments and experimental set-ups, along with theories, concepts and models that describe or explain these instruments and experiments, and, depending on where one starts the research, reference to the imagined phenomenon. Footnote 37

In sum, instead of a picture-like representational relationship between the imagined and the purported real-world target-phenomenon, both are conceptualized and identified through specific constellations within the discipline. This makes their representational relationship much more complex. At the same time, the conceptually rich representations of both imagined and real-world target-phenomena that are crafted with the help of a disciplinary perspective enable different kinds of inferential reasoning by researchers, leading to new creative ideas, relevant questions and challenging hypotheses.

6.4 A Kuhnian framework for characterizing disciplinary perspectives

As was suggested above, researchers in specialized fields or disciplines have developed and internalized perspectives that give direction to how they approach research, which is called a disciplinary perspective. Researchers have adopted the disciplinary perspective, often without being fully aware of it, since the perspective is not usually explicitly conveyed or reflected upon. A metaphor for this role of the disciplinary perspective in becoming a researcher is that they have learned to look through a specific kind of spectacles, which they now wear without noticing it. In a Kuhnian fashion, I suggest that disciplinary perspectives can be characterized as consisting of heterogeneous but mutually cohering elements that support and reinforce each other. These aspects are listed here:

Intrinsic aims and objectives related to what is considered the subject-matter of research in the discipline, usually reflected in the name of the discipline. For ex-ample, mechanics, chemistry, systems biology.

Practical purposes that are related to ideas about the extrinsic, practical relevance of the research-projects in the discipline. This purpose is usually reflected in the name of applied scientific fields, such as in technology (e.g., membrane technology), medicine (e.g., oncology, immunology), and agriculture (e.g., plant pathology). These disciplines raise research questions oriented at practical applications such as: “Can we improve the energy efficiency of steam engines?” “Can we bio-mimic photosynthesis to harvests sunlight?” “Can we find a drug for this type of cancer?” “Can we find a cure for alcohol addiction?” “Can we cultivate a species that is resistant to this pest?”

Research questions typical of the discipline, which are related to the intrinsic and extrinsic aims of the discipline. For example: “What is the mechanism that exp-lains these phenomena?” “Which genetic factors are related to alcohol addiction?” “Which factors in an individual’s personal history contributes to alcohol addicti-on?” Also see Carnot’s research question in note 26.

The types of real-world phenomena (observable and non-observable) typically investigated in the discipline. Usually, the observable phenomena are related to the kinds of phenomena described in the external, practical purposes or problems that the discipline aims to engage with (e.g., technological functions, illnesses, pests). The non-observable phenomena are related to the more fundamental ontology of the discipline. They are the kinds of things in terms of which the discipline aims to understand and intervene with the observable phenomena. For example: Chemistry studies phenomena related to molecules. Microbiology studies phenomena related to micro-organisms. Biochemistry studies phenomena related to biochemical pathways. Psychology studies phenomena related to (the behaviour of) individuals. In this article, I have suggested that non-observable phenomena are characterized and defined by a specific constellation within the discipline. This includes for example: the data and observable phenomena indicative of the non-observable phenomenon; the measurement instruments and procedures that produce these data and observable phenomena; experimental set ups by which the purported phenomenon is investigated; and, the scientific concept or phenomenological law ‘describing’ it (also see Feest 2010 ; Boon 2012 ).

Fundamental (ontological) principles, basic assumptions and beliefs used in the construction of scientific models and the conceptual articulation of non-observable phenomena. For example, fundamental principles in chemistry are conservation principles such as the principles that mass, atoms, charge and energy cannot appear or disappear.

Mathematical frameworks and axiomatic systems typical of the discipline. In my explanation of the semantic view, I suggested that mathematical equations entail variables and parameters that must be measurable in the discipline. For example, disciplines that make use of thermodynamics in their modelling of phenomena (e.g., when investigating aspects of ‘artificial photosynthesis’), will also need to have measurement procedures to determine the variables and parameters in the mathematical equations that represent the thermodynamic properties of these phenomena (e.g., voltage, wave-length, thermal and electrical energy). Therefore, the (sets of) equations produced through these frameworks are interconnected with measurements and instruments specified in aspect viii below.

Theoretical (conceptual) frameworks and empirical (phenomenological) knowledge accepted in the discipline, including specific scientific concepts indicating observable and non-observable phenomena, and other technical terms. For example, chemistry uses scientific concepts that refer to perceivable properties such as ‘colour,’ ‘acidity,’ ‘viscosity,’ ‘fluidity,’ and ‘crystallinity,’ and also to purported non-observable phenomena such as ‘atoms,’ ‘molecules’ and ‘chemical reactions.’ The conceptual framework also encompasses theories and models that represent aspects of these phenomena, such as causal mechanisms that are held responsible for specific properties or chemical conversions.

Measurement instruments and procedures used in the discipline, including prac-tical and theoretical knowledge about these techniques and procedures. For exa-mple, chemistry typically uses equipment such as a balance, a thermometer, a pH-meter, an Eh-meter, an oxygen-meter, a gas-flow meter, gas chromatography, and mass spectrometry. Researchers usually have practical and theoretical unders-tanding of the workings of this equipment.

Research methods and typical strategies to investigate the phenomena. For example, disciplines usually develop specific types of experimental set-ups to investigate phenomena of interest (i.e., aspect viii). These are usually reported in the materials and methods section of a scientific article. Additionally, these methods and strategies are interconnected with and enabled by aspects v-vii above and aspects x-xi below.

Epistemic and pragmatic criteria that epistemic results such as scientific models should meet. More general criteria that were already mentioned, are shared by most experimental practices. But also more specific criteria may apply, which have to do, for instance with the specific application context, such as ‘reliability,’ ‘simplicity,’ ‘applicability,’ ‘specificity,’ and ‘predictive power.’

Representational means typical of the discipline, such as mathematical models, computer simulations, causal-mechanistic models, specific types of diagrams, pic-tures, and physical models.

The listed aspects i-xi together characterize the disciplinary perspective. Footnote 38 Each of these aspects deserve further explanation. Also, their mutual relationships need to be worked out in more detail. Moreover, philosophers will undoubtedly wish to know how these aspects are justified, or whether they are necessary and sufficient. For the moment, my approach in listing is pragmatic, based on knowledge and understanding of existing research practices. Still some justification can be given. The reader may have noticed the overlap between this Kuhnian framework to analyse a disciplinary perspective, and the B&K method for constructing or reconstructing scientific models (note 19). This should indeed be the case because researchers need guidance in drawing up a scientific model by making decisions and choices about the aspects listed in the B&K method. The disciplinary perspective makes these decisions and choices possible. So, the disciplinary perspective guides and restricts the construction process. This means, therefore, that the elements indicated in the disciplinary perspective must cover the type of choices and decisions that are made when constructing scientific models.

Different scientific disciplines have different disciplinary perspectives, but I argue that each of them can be analyzed in terms of this Kuhnian framework (i.e., the aspects i-xi). The concrete, discipline-specific disciplinary perspective is what researchers in that discipline ‘have in mind.’ Researchers are trained in using the disciplinary perspective. This gives them direction in their research efforts, for example in how to set up a research project and how to perform practical and epistemic activities in the discipline. Most often, the disciplinary perspective ‘automatically’ gives direction to how researchers conduct their research. But the disciplinary perspective does not function as an algorithm. Scientific research still involves a process of searching, choosing, and ‘fitting together’ (cf., Hacking 1992 ). Researchers must solve puzzles and make creative leaps to generate scientific models that meet the requirements of the discipline. And although the number of options is limited within a discipline, researchers still have to make numerous choices and decisions about what and how they conduct the research. In the types of choices that must be made, the aspects i-xi of the disciplinary perspective are guiding and constraining, although researchers are usually not explicitly aware of this.

6.5 Evaluating disciplinary perspectives

The claim in this article is that scientific models are constructed within the boundaries of a discipline and guided by that discipline’s disciplinary perspective. This leads to an (epistemological) anti-realism, such as defended by Van Fraassen ( 1980 , 2008 ) and also Vaihinger ( 1911 ), which I endorse. The idea the disciplinary perspectives play an indispensable role suggests that each discipline can generate completely different and even incompatible models of reality. This consequence may give leeway to harmful forms of subjectivism and relativism. I contend that this can be avoided in different kinds of ways. Firstly, the construction process and the resulting scientific models must adhere to pragmatic and epistemic criteria that apply within the discipline (aspect x). Secondly, several of the epistemic criteria operative in a scientific discipline transcend the specific disciplinary perspective. For example, internal coherence, logical consistence and empirical adequacy. Additionally, some of the theoretical frameworks transcend specific disciplines. Thirdly, the present article focuses on scientific practices that aim at scientific models (amongst other things) for adequately dealing with real-world problems (i.e., aspect ii). The focus on societally relevant epistemic purposes may allow for dealing in a more pragmatic fashion with controversies. Nevertheless, because of this societal purpose of scientific research, it is crucial that scientific results are critically evaluated with regard to epistemic and pragmatic criteria (i.e., aspect x) that best fit the intended (societal) purpose. Fourth, I suggest that the disciplinary perspective can and should be analyzed and evaluated, for which this Kuhnian framework is proposed. Therefore, the disciplinary perspective can be critically examined, for example, by revealing erroneous (e.g., empirically testable) assumptions, logical inconsistencies, and incoherencies between elements (i.e., between the aspects i-xi). Also, shortcomings of methods or knowledge used in the discipline may be pointed out, which can result into improvements of specific aspects in this list. Footnote 39 In this manner, by critically analyzing the disciplinary perspective and also learning from other disciplines, scientific disciplines can evolve and become enriched, refined, and/or (more) adequate for (additional) epistemic tasks.

7 Overview and conclusions

In Section 2 , the semantic view of theories is interpreted as an epistemology of models. This leads to three important insights that come back throughout the article when explaining problematic aspects of scientific models.

The first insight is that, to understand ‘how it is possible that models represent their target,’ it is crucial to presuppose (as the semantic view does) that models are non-linguistic entities , because this makes possible the comparison between models derived from abstract theories and data-models derived from experiments. Footnote 40 Hence, due to the fact that models, according to the semantic view, are (non-linguistic) structures it is possible to easily compare them and to decide whether they are similar in the sense of being (partly) isomorphic. From a scientific practice point of view, therefore, the representational relationship between the model and its target would be more or less unproblematic if models were mere non-linguistic entities.

The second insight is that the role of measurable variables is crucial for explaining how it is possible that a relationship can be established between a model and the real world. In the semantic view, the phenomenon that is imagined when deriving a model from the abstract theory is characterized in terms of measurable variables . In order to be comparable, the data-model that represents the real-world phenomenon must be characterized by the same measurable variables . This is how a semantic connection between them can be established. Otherwise, no comparison between them would be possible. Therefore, the representational relationship between the model and the target-phenomenon is based on representations in terms of measurable variables. Here as well, the representations being (non-linguistic) structures makes this relationship and comparison possible.

The third insight is that models derived from abstract theory, only represents the imagined phenomenon as a model-outcome , for example at (physical-technological) conditions in the experimental set-up. Therefore, models as ‘direct’ or ‘literal’ representations of (purported) non-observable phenomena is not an issue in the semantic view.

Section 3 discusses in what sense the notion of models as independent epistemic entities differs from the notion of models in the semantic view. Morgan and Morrison (eds. 1999 ) have argued that scientific models are not always derived from theories, but also have a ‘life of their own.’ That is why they regard models as ‘autonomous epistemic entities,’ which is a view that I accept as more suitable for scientific practice. But this implies that the semantic view, although cogent, provides a too limited view of the epistemic roles of models in scientific practice.

This requires rethinking how models represent. I explain why philosophical accounts that take scientific models as (more or less) literal representations prove to be problematic. This is particularly the case for models that represent purported (aspects of) real-world target-phenomena that cannot be perceived in a straightforward manner. Footnote 41 Moreover, this also requires rethinking how it is possible to identify the phenomenon that is represented by the model, independent of that model. In addressing this, I conclude that an epistemology of scientific models also requires an epistemology of phenomena .

Section 4 explores ideas about phenomena in the philosophy of science in order to get a grip on the question how it is possible that scientific models represent non-observable target-phenomena. Here, the key-issue is how we get to know the real-world phenomenon that is represented by the model. First, I refer back to the concept of phenomenon in the semantic view in which this concept seems unproblematic. In part, this is because phenomena are assumed non-linguistic observable entities (i.e., structures in terms of measurable variables).

I then show that there are various interdependent issues that make both the concept of phenomenon, and the idea that models represent phenomena, rather blurry. Van Fraassen ( 1980 , 2008 ) argues that phenomena are observable by definition, and denies unobservable phenomena. Bogen and Woodward ( 1988 ) aimed at a richer concept that agrees better to notions of phenomena in scientific practice. They assume that most phenomena are not observable in a straightforward manner. However, the distinction between data and phenomena proposed by them runs into trouble when they aim to avoid that phenomena are ‘low-level’ theories by assuming that phenomena are (derived from) patterns in data. As a consequence, their approach returns to the notion of phenomena in the semantic view. Still another idea about phenomena and their role in scientific practice is that phenomena are real-world things or occurrences that arouse our interest and ask for an explanation (Hacking 1983 ). In this view, scientific research starts with observed phenomena, rather than theories or data, and seeks to find explanations for these phenomena. This is an important addition, because research practices that target practical problems often start from thinking about phenomena. These can be phenomena observed in nature or produced in a laboratory. Moreover, the point of departure in a research project may also be non-observable phenomena that are postulated to explain observable phenomena. Footnote 42

The issue raised by pointing out the different roles of phenomena in scientific practice is that scientific models represent the target-phenomenon not only in the sense of a (‘literal’) description or picture, but also in the sense of presenting an explanation for the phenomena. In the latter case, the scientific model that explains an observable target-phenomenon 1 supposedly represents a non-observable phenomenon 2 .

Based on this exploration of ideas about phenomena, it becomes obvious that phenomena, as objects of study in scientific practice, do not appear to researchers as ‘ready-made’ entities (Massimi 2007 , 2008 , 2011 ). Someone cannot simply point at a phenomenon and then photograph or draw or describe it. Instead, researchers inescapably use (theoretical) concepts to identify and conceptualize a phenomenon, usually within their own disciplinary perspective . The idea of ‘scientific models as literal representations of target-phenomena’ is therefore misleading. It wrongly suggests that researchers first point at a phenomenon, and then represent it – where this representation is the model. Altogether, I conclude that usually it is not possible to identify a phenomenon independent of any conceptual, theoretical or mathematical framework. This also implies that the identification of the target-phenomenon (be it ‘observable’ or ‘non-observable’) must be understood as an inherent aspect of modelling it (as explained in Section 5 ). Furthermore, the way in which that is done is guided and restricted by the disciplinary perspective (Section 6 ).

Section 5 argues that avoiding the picture-metaphor (i.e., the assumption of a similarity relationship between model and target) raises a number of philosophical issues (a-f) that an epistemology of models needs to address, and suggests that dealing with these issues requires taking into account the construction of models instead of starting from ready-made models. A recently published method for (re)constructing scientific models (Boon 2020 ) is taken as an example for this purpose (see note 19). According to this method, models initially form hubs in which heterogeneous aspects are brought together and integrated into a coherent whole. This account of how scientific models are constructed agrees with various of the insights developed in the present article. In particular, the method assumes that the construction of a scientific model involves the identification and conceptual articulation of the (imagined) target-phenomenon. Furthermore, the method requires specifying the physical or technological conditions that are considered relevant to the target-phenomenon, together with the measurable variables that characterize it, which complies with the proposed explanation of how the imagined phenomenon is connected with the real-world phenomenon.

It is explored how the construction of models according to this method of construction sheds light on the philosophical issues (a-f) relevant for an epistemology of scientific models, based on which several conclusions can be drawn: (1) The method of construction shows that scientific models are not somehow read from reality (like a photograph or drawing). (2) The insight that constructing a model involves combining heterogeneous aspects makes it clear that both non-linguistic and conceptually meaningful linguistic content is selected and built into the model. This implies that the scientific model is not merely a (non-linguistic) picture or graph or set of mathematical equations that somehow literally represents the (objective) structure of a real-world target-phenomenon. Instead, the model is more like a coherent story that contains linguistic and non-linguistic content presented in, for example, a scientific article. Moreover, the way a scientific model is constructed depends on contextual information, which is related to the specificities of the discipline and to the epistemic purpose of the research project. Therefore, it makes sense to assume that a scientific article in its entirety presents the scientific model. (3) The method of construction explains how it is possible to connect between the model and the purported non-observable real-world phenomenon . This accords with my take on the semantic view in which the role of measurable variables is critical. However, the conceptual articulation of the phenomenon is also crucial, in particular to enable different types of inferential reasoning by means of the model. This requires to also explain how conceptually meaningful content enters the model. (4) In Section 4 it is argued that the target-phenomenon is not ‘ready-made,’ but requires conceptual articulation. In line with this insight, Section 5 explains that, according to the method of construction, the identification of the phenomenon is an inherent part of the construction process. The conception of the target-phenomenon (i.e., the imagined phenomenon) and the model are therefore co-constructed. For example, the construction of a model starts with observations, experiences, or data that a researcher wants to deal with, and is often also related to a broader (practical or theoretical) problem. In conceptual articulation, these observations, experiences, or data are brought under (scientific) concepts (i.e., ‘subsumption under concepts’). This is one way how conceptual content enters the model. (5) The method of construction explains how a model is justified . I distinguish three ways that complement each other. First, there is the comparison between model outcomes and experimental data (as in the semantic view). But it must also be assessed whether the model meets relevant epistemic and pragmatic criteria. ‘Internal coherence,’ for example, warrants that the model enables inferential reasoning. In addition, the construction of a model requires that the choices and decisions that are made with regard to the various elements built into the model be justified. (6) Lastly, therefore, it is argued that an epistemology of scientific models should also explain and justify the choices and decisions from researchers on aspects that must be built into the model. Moreover, researchers need to somehow interpret empirical and experimental findings (e.g., subsumption under concepts to imagine the target-phenomenon), for which intellectual capabilities of researchers are crucial, such as imagination . Footnote 43 Therefore, in accordance with the transcendental and pragmatic approach adopted here, an epistemology of models requires further clarification as to how the choices, decisions and conceptualizations by researchers are made possible. To explain this in more depth, I claim that the construction of scientific models is enabled and guided, but also restricted, by the disciplinary perspective within which researchers work. Section 5 , “Taking Stock” summarizes the aspects of an epistemology of scientific models in a number of statements.

The second part of this article concerns the role of (disciplinary) perspectives in developing an epistemology of models. The approach is again transcendental and pragmatic. I side with Massimi and McCoy (eds. 2020 , 4) who state that: “[U]ltimately it does not matter how one defines the notion of “scientific perspective” (e.g., à la Giere, or à la van Fraassen, among others; with reference to scientific models, Kuhnian paradigms, or concepts and conceptual schemes). What matters most is what perspectivism can achieve, how it enters the practice of science, the challenges it poses, and the solutions it offers.” Nonetheless, I take it that it needs to be explained why we need disciplinary perspectives in an epistemology of scientific models. According to Massimi and McCoy, the practice orientation stresses the human point of view and therefore the role of perspectives. Similarly, my philosophical argument for defending the indispensable role of perspectives is based on the conclusion that the philosophically problematic representational relationship between model and target, and issues that arise from giving up the picture-metaphor of representation, must be clarified by explaining how it is possible that researchers construct scientific models, and how it is possible that these models can be used for inferential reasoning in performing epistemic tasks.

Section 6 , therefore, explains the epistemological and pragmatic roles of (disciplinary) perspectives in the construction of scientific models. It starts from asking: What are perspectives, and why do we need them? First, I argue that this is because perspectives in the broad sense enable and constrain the epistemic activities of researchers when constructing and using scientific models. Building on Giere’s ( 2006 ) work on perspectives in science, I distinguish five types of perspectives and explain their roles with regard to the issues addressed in the first part of this article. Then, I suggest that these different types of perspectives are part of the disciplinary perspective of a discipline. Next, I propose a Kuhnian framework for characterizing disciplinary perspectives in the form of a preliminary scheme that lists elements typically included in disciplinary perspectives. In short, the Kuhnian framework consists of a coherent set of heterogeneous elements, which includes the types of problems, phenomena, fundamental and ontological beliefs, measurement techniques, experimental procedures, mathematical, theoretical and conceptual frameworks, investigative strategies, and representational means that are typical of the discipline. The elements in the Kuhnian framework partly reflect those in the method for constructing models. This coherence explains why disciplinary perspectives understood in this way make the (discipline-specific) construction of scientific models possible and also limit it.

Finally, it needs to be explained how to deal with cherished values such as objectivity , which in more traditional views of science requires “a view from nowhere” (Massimi and McCoy eds., 2020 , 2). Objectivity seems to require that knowledge is not constructed through perspectives. I defend that disciplinary perspectives are indispensable for the construction of models These contributions from researchers are indelibly built into the resulting scientific model and makes it ‘discipline-specific,’ rather than objective. To deal with this challenging issue and prevent harmful subjectivism, my suggestion is that in scientific research, disciplinary perspectives can and should be made explicit and critically evaluated, for which the Kuhnian framework proposed here may prove helpful.

My focus on scientific practice includes a normative stance in the sense that philosophical accounts must be adequate and relevant for (specific types of) scientific practice.

See Frigg and Hartmann ( 2018 ) and Frigg and Nguyen ( 2016b ) for comprehensive overviews.

Alternatively, philosophers have proposed that models are fictions (e.g., Suárez, ed., Suárez 2009a , b ). In the present article, I will focus on the idea that models are representations.

A Kantian (transcendental) approach can be compared with approaches in science that aim at explanations . In the natural sciences these explanations cannot be ‘read’ from nature. Therefore, researchers ask what must be presupposed about the (purported) underlying (non-observable) structure of nature to explain observable events. In a similar way, a transcendental approach in philosophy starts from asking what must be presupposed about human cognition and their epistemic strategies to explain epistemic results. Researchers can only assess an explanations for its value to the intended (epistemic or pragmatic) uses. Hence, the suggested similarity between approaches in the natural sciences to look for explanations, on the one hand, and transcendental approaches in philosophy, on the other, agrees to an anti-realist epistemology according to which humans are ‘in principle’ unable to determine whether the proposed explanation is literally true. This anti-realist (and anti-metaphysical) assumption about both science and philosophy guides my approach throughout the present study.

Also see Suppe ( 1989 ) for a comprehensive explanation of the Semantic View of Theories.

More specifically, a number of representational relationships play a role in this account of testing abstract theories: (a) the representational model representing the abstract theory; (b) the representational model representing the imaginary phenomenon; (c) the real-world phenomenon generated in the experimental set-up representing the imaginary phenomenon, vice versa; (d) the raw data representing the real-world phenomena; and (e) the data-model representing the raw data. Each of the sentences (a)-(e) has the form “A represents B.” Eventually, the test of the theory is by comparison between two structures : (f) the representational model is partially isomorphic or structurally similar to the data-model, having the form “A is / is not partially isomorphic (or structurally similar) to B.” When assuming that the representational relationships (a-e) are (partial) isomorphic relationships between structures, and also, that isomorphic relationships are transitive (i.e., if structure A is isomorphic to structure B, and B is isomorphic to C, then A is also isomorphic to C), then this way of reasoning to test or justify the abstract theory is sound.

Instead of representational model (as in the semantic view), I will use the notion scientific model .

My emphasis on the crucial role of measurements and experimentation in generating representations that allow for comparison agrees with Van Fraassen’s ( 2008 ): “it is not only to our understanding of theories and their models that representation is relevant. The achievement of theoretical representation is mediated by measurement and experimentation, in the course of which many forms of representation are involved as well ,” (ibid, 2, my emphasis).

See Frigg and Hartmann ( 2018 ) for a comprehensive overview of philosophical discussions on models in science.

A notable exception is Bailer-Jones ( 2009 ).

I agree with Van Fraassen ( 2008 ) on the view that phenomena created by instruments are observable phenomena : “our instruments are engines of creation . They create new observable phenomena, ones that may never have happened in nature… Those new phenomena are themselves observable, and become part of our world,” (ibid, 87).

See Chapter 7 in Bailer-Jones ( 2009 ) on phenomena, data and data-models.

In this article, the notions ‘inferential reasoning’ (Suárez 2004 ) and ‘epistemic activities’ (Chang 2014 ) are used. Inferential reasoning according to Suárez assumes reasoning upon (non-linguistic) structures, whereas epistemic activities in the sense of Chang is a more open notion. I will use the two notions interchangeably, assuming that inferential reasoning is not limited to reasoning based on structures, but also based on, for example, conceptual content.

My position agrees in many respects with Vaihinger’s ( 1911 ) philosophical view on ‘as if’ reasoning. This views, in turn, is close to current epistemological interpretations of Kant’s philosophy of science that I endorse. Vaihinger’s ideas have been taken up in the current movement of models as fictions (e.g., Fine 1993 ; Suárez 2009a , b ). Although relevant to the issues at stake, I will not elaborate on the idea of models and representations as fictions in the present article.

More specifically on the meaning of purported non-observable real-world phenomena: I do not endorse a form or referential realism (see Teller 2020 for a plausible argument against referential realism ). Instead, my position agrees in many respects with Van Fraassen’s (epistemological) anti-realism. This position emphasizes a common-sense realism, not in the sense of believing that the purported phenomenon has a referent in the real world, but rather in the sense that observations and experiences of researchers derive from their (physical and technological) interactions with a world that is physically independent of their thoughts and beliefs .

The concept of representations of phenomena presented here is close to accounts of scientific concepts by Nersessian ( 2009c ), Feest ( 2010 ), and Boon ( 2012 ). The crux is that the mathematical, theoretical and conceptual content that is built in the conception of phenomena (although based on ‘raw’ data) is partly hypothetical and fictional (e.g., Vaihinger 1911 ), rather than fully empirically grounded (as in the semantic view). Nonetheless, the resulting representation must meet pragmatic and epistemic criteria such as internal coherence and logical consistence, intelligibility in the sense of physical conceivability (Massimi forthcoming ), physical plausibility, and empirical adequacy (Van Fraassen 1980 ). Additionally, it is precisely thanks to the conceptually meaningful but still hypothetical and fictional content built into the representation of the phenomenon that different kinds of inferential reasoning are made possible. Outcomes of inferential reasoning based on the representation of the purported real-world non-observable phenomenon can be tested against reality. In this manner, it is tested whether the hypothetical content and what this content allows to infer from it meets epistemic criteria such as empirical adequacy.

Clearly, the first and the second model are not identical. Further analysis could aim to better understand their semantic and epistemological relationships. This issue will not be elaborated here.

Also see Bokulich ( 2009 ), who sees the explanatory power of models as being closely related to their fictional nature.

The proposed B&K method consists of ten questions to systematically determine the concrete aspects that are built into the scientific model (Boon 2020 ). This list can be employed to construct a model but also to reconstruct how an existing model was put together. In short these questions are: What is/are the:

Problem context (which may refer to the socio-technological problem)?

Target-system or physical-technological phenomenon (P) for which the model is constructed?

Intended epistemic function (s) of the model? (which refers to inferential reasoning in regard to the problem stated in aspect 1).

Model type ? (for example, a causal mechanist, or a mathematical model; this choice is related to the intended epistemic function).

Relevant (physical and/or technical) circumstances and properties (e.g., by which variables is a non-observable phenomenon connected to the tangible world, or, by which variables is the phenomenon or target-system affected)?

Measurable (physical-technological) variables (i.e., how is the phenomenon identified or connected to the tangible world)?

Idealizations, simplifications and abstractions (e.g., concerning aspects 2, 5 and 8)?

Knowledge used in the construction of the model (e.g., theoretical principles and knowledge, knowledge of sub-phenomena, phenomenological laws, empirical knowledge)?

Hypotheses (e.g., new concepts and explanations) built into the model?

Justification or testing of the model? (Also see Section 5 , issue e).

A notable exception is Contessa ( 2010 ), who discusses the ontological status of models, thus distinguishing between three types of models (material, mathematical, and fictional). Contessa argues that the ideal pendulum described in physics textbooks is not a material, nor a mathematical but rather a fictional object. Accordingly, Contessa argues that fictional models represent fictional entities. My notion of imaginary or imagined phenomena appears close to Contessa’s notion of fictional objects. However, although Contessa raises the question of how the model refers to the real world, he does not provide a satisfactory account of the semantic and epistemic relationships between fictional entities (which in his view are imaginary objects, such as the ideal pendulum) and real-world objects. Also see Toon ( 2012 ).

The claim that the construction of a model first requires a representation of the target-phenomenon seems confusing with regard of the general idea that models are representations of their target-phenomenon . Two responses are possible. Firstly, the representation of the target-phenomenon can indeed already serve as a preliminary model (which is an idea proposed in Knuuttila and Boon 2011 ). Secondly, as already indicated in Section 4 , scientific models are often more than a strict (e.g., ‘literal,’ ‘picture-like,’ or ‘descriptive’) representation of the phenomenon, because models usually offer explanatory or theoretical (e.g., mathematical) interpretations of the target-phenomenon. In this case, the phrase “the model represents the target-phenomenon” actually means that the model represents an explanation of the phenomenon. Pushing this further, it can also be said that “the model represents the phenomenon 2 (e.g., a causal mechanism) that explains the target-phenomenon 1 .”

Carnot’s construction of the model of the ideal heat engine is a case that cannot be grasped by the original semantic view of theories, because the model is not derived from an abstract theory. Instead, thermodynamic theory emerged from Carnot’s model of the ideal heat engine. Unfortunately, many textbooks in thermodynamics present the ideal heat engine as if it derives from thermodynamic theory, that is, as if thermodynamic theory made the invention of heat engines possible.

Frigg and Nguyen ( 2016a ), in their DEKI account of representation, use Kendrew’s plasticine model of myoglobin as an example. In this example, myoglobin is the target-system T. Hence, in my vocabulary, myoglobin, is the purported non-observable real-world target-phenomenon that is represented in the model. According to Frigg & Nguygen, the model M denotes its target system T, and denotation is the core of representation. However, although their DEKI account of representation claims that denotation of the target-system is crucial to modelling, they do not explain how the target system T is denoted, i.e., how it is possible that scientific researchers denote, identify, indicate, or whatever you call it, a target-phenomenon. This problem is at the core of my article.

Vaihinger ( 1911 ), in a Kantian fashion, stressed the importance of subsumption under concepts , which is a crucial part of his notion of ‘as if’ reasoning in scientific practice. By bringing observations or experiences under a concept an imaginary phenomenon is generated (e.g., a regularity, a law, an invisible entity, or a property). Subsequently, the structure or content of the concept enables epistemic agents to reason about that imaginary phenomenon – i.e., it enables inferential reasoning through the structure and content introduced by the concept. It is important to see that in this manner, Vaihinger defends an anti-realist position in the sense that subsumption under concepts enables ‘as if’ rather than ‘it is’ reasoning.

My use of the words ‘conception’ or ‘conceptualize’ is similar to Rouse’s ( 2011 ) notion of ‘conceptual articulation.’ I use the two notions interchangeably.

The practical problem is improving the ‘useful effect’ (which at some point got translated into ‘energy-efficiency’) of real heat engines. Carnot translates this into a theoretical problem as follows: “The question whether the motive power of heat [i.e. the useful effect that a heat engine is capable of producing] is limited or whether it is boundless has been frequently discussed. Can we set a limit to the improvement of the heat-engine, a limit which, by the very nature of the things, cannot in any way be surpassed? Or conversely, is it possible for the process of improvement to go on indefinitely?” [Sadi Carnot (1824), Reflexions on the Motive Power of Fire and on Engines fitted to develop that Power ].

These epistemic criteria ( intelligibility and physical plausibility ) seem to resonate with Massimi’s ( forthcoming ) notion of physical conceivability .

Note that the semantic notions isomorphism and similarity to describe the semantic relationship between model and world only applies to this third way of testing a scientific model, that is, to the semantic relationship between the model-outcome and the experimental-outcomes. This accords with ideas about testing against reality in the semantic view. However, this is only one part of the testing of a model. It is important to recognize that the other two ways of testing are not through somehow assessing isomorphism or similarity between model and world.

Philosophers may disagree whether this question is worth philosophical analysis. Giere, for example, rhetorically asks: “Do we, as theorists of science, need to give a more detailed account of the processes of interpretation..? I think not. We can pass this job off to linguists and cognitive scientists. We know it can be done because it is done” (Giere 2010 , 271). Similarly, Suárez ( 2012 ), in his review of Bailer-Jones’ monograph, denies that her “burning question” deserved explanation. Her burning question is: “How is it that there is something about the model that allows us to demonstrate something that then, after appropriate interpretation, becomes applicable to and insightful about real-world phenomena?” (Bailer-Jones 2009 , 197). Both Giere ( 2010 ) and Suárez ( 2004 ) thus shift the question of how it is possible that models allow for inferential reasoning to the competent and informed agent. Clearly, I disagree with them on this issue. With regard to scientific practices this is the fascinating and difficult to solve aspect of scientific representation. In particular, when models supposedly represent target-phenomena that cannot be observed in a direct and straightforward way (such as DNA), it is difficult to understand, even for competent researchers, how the scientific model is similar to its target-phenomenon or why its structure allows correct inferential reasoning about it. Their accounts, therefore, are not very informative about the epistemic functioning of models and modelling in scientific practice.

Also see Toon ( 2012 ), who argues that the more sophisticated version of Giere’s ( 2010 ) similarity view that appeals to the role played by scientists and their representational capacities is not yet sufficient. Instead, we must describe how it is that scientists use models to represent, and proponents of the similarity view “must offer a different account of how similarities are put to work in scientific representation” (ibid, 255).

The idea that the conceptual articulation (and representation) of the target-phenomenon is an inherent part of the model, to some extent corresponds to ideas that are put forward in a fiction-view of models. However, my worry is that fiction views of models do not explain the semantic and epistemic relationship between the imaginary phenomenon represented in the model and the real-world target-phenomenon.

My notion of scientific practice and epistemic activities agrees in many respect with Chang’s ‘system of practice’ ( 2014 , 2020 ).

According to Giere ( 2006 ), this phenomenon is then interpreted as a Newtonian system. Hence, Newton’s theory is used as a theoretical perspective to generate a scientific model of the phenomenon, i.e., a representation that interprets or structures the phenomenon in a very specific way. Vaihinger ( 1911 ) would rather say that the phenomenon is subsumed under Newton’s laws of motion. The resulting scientific model then allows ‘as if’ reasoning about the phenomenon.

Boon and Knuuttila ( 2009 ) and Knuuttila and Boon ( 2011 ) call models epistemic tools . In fact, they thereby point at this capacity of scientific models to be used (in the current vocabulary) as perspectives in epistemic tasks.

These examples of relevant aspects form the disciplinary perspective within which Carnot conceptualized the phenomenon (“heat is converted into motive-power”) and constructed the model of the ideal heat engine. They can be found in Carnot’s (1824) treatise.

One of the reasons for endorsing perspectivism in the philosophical literature, is the concern that scientific practices use multiple conflicting models to explain and understand the same phenomenon (e.g., Rice 2020 ; Fagan 2020 ; Mitchell 2020 ). However, when accepting my suggestion that unobservable phenomena are defined and characterized within a complex constellation of different aspects, authors could look more critically at what “the same phenomenon” means. Wolff ( 2020 ) provides a comprehensive point of departure for such an investigation. ‘Alcohol addiction’ is my own simple example to show that ‘the same phenomenon’ may be a problematic notion, because this ‘observable’ phenomenon is turned into a much more sophisticated conception when studied in either sociology, psychology, neurobiology, or genetics. In each of these scientific disciplines, the conception of the phenomenon is linked to theoretical concepts and measurement procedures of that discipline, as well as to the specific research questions asked in the discipline, usually in view of the practical (societal) problem. Therefore, it is not at all ‘the same phenomenon’ that is modelled in different disciplines.

In several respects my view of phenomena corresponds with Rouse’s ( 2009 , 2011 ) ideas about the roles of scientific concepts and hypothetical entities in scientific practice. According to Rouse, laboratory work and experiments play a crucial role in articulating and consolidating conceptual understanding. He stresses that experimentation is integral to conceptual articulation of the phenomenon. This also involves the idea that activity and practice precede ontology , an idea that I endorse in the present article. Accordingly, in my terminology, phenomena are postulated in the interaction between experimenting, measuring and conceptualizing. It is through the intellectual activities of researchers that a (non-observable) real-world phenomenon is conceptually postulated, while at the same time the purported phenomenon is physically determined, characterized and established by the researchers’ practical activities (e.g., experimenting and measuring).

This list of elements (i-xi) is not meant to be exhaustive, nor will all these elements always be present or relevant when analysing and articulating a disciplinary perspective.

For example, an established discipline may incorporate new methodologies, mathematical frameworks, scientific concepts, theories and measurement techniques taken from other disciplines (Boon and Van Baalen 2019 ). An example of the transfer of methods, and the theoretical frameworks and measurement techniques that accompany these methods, is traditional scientific practices such as biochemistry that have evolved into biotechnology and systems biology. The traditional practice typically used experimental methods to produce causal-mechanistic models, but at some point, these practices adopted mathematical methods, which provided new opportunities, including mathematical models of the same system that allow for different epistemic uses.

In the philosophy of science, scientific models are usually interpreted as non-linguistic entities, that is, picture, graphs, diagrams or 3D-objects that ‘speak for themselves.’ In short, non-linguistic entities consist of images (e.g., of a phenomenon in the real-world that people can perceive by observing it), whereas linguistic entities consist of descriptions (e.g., of a phenomenon in the real-world that people grasp by reading the text).

In this article, real-world target-phenomena that cannot be perceived in a straightforward manner are called ‘purported non-observable real-world target-phenomena.’ Focus is on physical or physical-technological real-world target-phenomena, but in some of the examples, I also refer to social phenomena.

Research could also aim at phenomena that do not even exist yet, but that are thought to serve some practical purpose. An example concerns the practical socio-technological problem of carbon dioxide emission in the production of electrical energy. An imagined solution is to harvest sun-light through artificial photosynthesis for the production of electrical energy. “Harvesting sun-light etc.” and “artificial photosynthesis” are examples of phenomena of interest. So far, they are imagined phenomena, and scientific research aims at knowledge to actually create them. Research in the engineering sciences thus results in scientific models of the imagined phenomena. These models must be such that they make it possible to actually create the imagined phenomena with technological means (Boon 2017 ).

The role of imagination introduced here goes beyond its role in the idea of models as fiction, as in Barberousse and Ludwig ( 2008 ), who investigate what it means to say that ‘models are fictions,’ and claim that the role of models in scientific practice lies in the activity of imagining . According to them, models are artifacts that enable researchers to play and experiment with ideas. In part, I agree with their idea in the sense that their notion of imagination is more or less synonymous with the notion of inferential reasoning that I use. But, by interpreting models as fictions, they avoid the philosophical premise that models must be understood primarily in terms of a putative referential relationship between the model and the purported ‘real-world’ phenomenon. In contrast, I aim at an epistemology of models that also explains how models make inferential reasoning about the real-world phenomena possible. In addition, I stress that imagination plays a role in the construction of models and the conception of phenomena.

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Acknowledgements

An earlier version of this paper has been presented at the Models and Simulation conference (MS8, 2018, University of South Carolina, Columbia, South Carolina). This work is financed by an Aspasia grant (409.40216) of the Dutch National Science Foundation (NWO) for the project Philosophy of Science for the Engineering Sciences . I wish to thank Michaela Massimi, Henk Procee and two anonymous reviewers for constructive suggestions.

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Boon, M. The role of disciplinary perspectives in an epistemology of scientific models. Euro Jnl Phil Sci 10 , 31 (2020). https://doi.org/10.1007/s13194-020-00295-9

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How To Write a Concept Paper for Academic Research: An Ultimate Guide

A concept paper is one of the first steps in helping you fully realize your research project. Because of this, some schools opt to teach students how to write concept papers as early as high school. In college, professors sometimes require their students to submit concept papers before suggesting their research projects to serve as the foundations for their theses.

If you’re reading this right now, you’ve probably been assigned by your teacher or professor to write a concept paper. To help you get started, we’ve prepared a comprehensive guide on how to write a proper concept paper.

Related: How to Write Significance of the Study (with Examples)

Table of Contents

What is the concept paper, 1. academic research concept papers, 2. advertising concept papers, 3. research grant concept papers, concept paper vs. research proposal, tips for finding your research topic, 2. think of research questions that you want to answer in your project, 3. formulate your research hypothesis, 4. plan out how you will achieve, analyze, and present your data, 2. introduction, 3. purpose of the study, 4. preliminary literature review, 5. objectives of the study, 6. research questions and hypotheses, 7. proposed methodology, 8. proposed research timeline, 9. references, sample concept paper for research proposal (pdf), tips for writing your concept paper.

Generally, a concept paper is a summary of everything related to your proposed project or topic. A concept paper indicates what the project is all about, why it’s important, and how and when you plan to conduct your project.

Different Types of the Concept Paper and Their Uses

This type of concept paper is the most common type and the one most people are familiar with. Concept papers for academic research are used by students to provide an outline for their prospective research topics.

These concept papers are used to help students flesh out all the information and ideas related to their topic so that they may arrive at a more specific research hypothesis.

Since this is the most common type of concept paper, it will be the main focus of this article.

Advertising concept papers are usually written by the creative and concept teams in advertising and marketing agencies.

Through a concept paper, the foundation or theme for an advertising campaign or strategy is formed. The concept paper can also serve as a bulletin board for ideas that the creative and concept teams can add to or develop. 

This type of concept paper usually discusses who the target audience of the campaign is, what approach of the campaign will be, how the campaign will be implemented, and the projected benefits and impact of the campaign to the company’s sales, consumer base, and other aspects of the company.

This type of concept paper is most common in the academe and business world. Alongside proving why your research project should be conducted, a research grant concept paper must also appeal to the company or funding agency on why they should be granted funds.

The paper should indicate a proposed timeline and budget for the entire project. It should also be able to persuade the company or funding agency on the benefits of your research project– whether it be an increase in sales or productivity or for the benefit of the general public.

It’s important to discuss the differences between the two because a lot of people often use these terms interchangeably.

A concept paper is one of the first steps in conducting a research project. It is during this process that ideas and relevant information to the research topic are gathered to produce the research hypothesis. Thus, a concept paper should always precede the research proposal. 

A research proposal is a more in-depth outline of a more fleshed-out research project. This is the final step before a researcher can conduct their research project. Although both have similar elements and structures, a research proposal is more specific when it comes to how the entire research project will be conducted.

Getting Started on Your Concept Paper

1. find a research topic you are interested in.

When choosing a research topic, make sure that it is something you are passionate about or want to learn more about. If you are writing one for school, make sure it is still relevant to the subject of your class. Choosing a topic you aren’t invested in may cause you to lose interest in your project later on, which may lower the quality of the research you’ll produce.

A research project may last for months and even years, so it’s important that you will never lose interest in your topic.

• Look for inspiration everywhere. Take a walk outside, read books, or go on your computer. Look around you and try to brainstorm ideas about everything you see. Try to remember any questions you might have asked yourself before like why something is the way it is or why can’t this be done instead of that . 
• Think big. If you’re having trouble thinking up a specific topic to base your research project on, choosing a broad topic and then working your way down should help.
• Is it achievable? A lot of students make the mistake of choosing a topic that is hard to achieve in terms of materials, data, and/or funding available. Before you decide on a research topic, make sure you consider these aspects. Doing so will save you time, money, and effort later on.
• Be as specific as can be. Another common mistake that students make is that they sometimes choose a research topic that is too broad. This results in extra effort and wasted time while conducting their research project. For example: Instead of “The Effects of Bananas on Hungry Monkeys” , you could specify it to “The Effects of Cavendish Bananas on Potassium-deficiency in Hungry Philippine Long-tailed Macaques in Palawan, Philippines”.

Now that you have a general idea of the topic of your research project, you now need to formulate research questions based on your project. These questions will serve as the basis for what your project aims to answer. Like your research topic, make sure these are specific and answerable.

Following the earlier example, possible research questions could be:

• Do Cavendish bananas produce more visible effects on K-deficiency than other bananas?
• How susceptible are Philippine long-tailed macaques to K-deficiency?
• What are the effects of K-deficiency in Philippine long-tailed macaques?

After formulating the research questions, you should also provide your hypothesis for each question. A research hypothesis is a tentative answer to the research problem. You must provide educated answers to the questions based on your existing knowledge of the topic before you conduct your research project.

After conducting research and collecting all of the data into the final research paper, you will then have to approve or disprove these hypotheses based on the outcome of the project.

Prepare a plan on how to acquire the data you will need for your research project. Take note of the different types of analysis you will need to perform on your data to get the desired results. Determine the nature of the relationship between different variables in your research.

Also, make sure that you are able to present your data in a clear and readable manner for those who will read your concept paper. You can achieve this by using tables, charts, graphs, and other visual aids.

Related: How to Make Conceptual Framework (with Examples and Templates)

Generalized Structure of a Concept Paper

Since concept papers are just summaries of your research project, they are usually short and  no longer than 5 pages. However, for big research projects, concept papers can reach up to more than 20 pages.

Your teacher or professor may give you a certain format for your concept papers. Generally, most concept papers are double-spaced and are less than 500 words in length. 

Even though there are different types of concept papers, we’ve provided you with a generalized structure that contains elements that can be found in any type of concept paper.

The title for your paper must be able to effectively summarize what your research is all about. Use simple words so that people who read the title of your research will know what it’s all about even without reading the entire paper. 

The introduction should give the reader a brief background of the research topic and state the main objective that your project aims to achieve. This section should also include a short overview of the benefits of the research project to persuade the reader to acknowledge the need for the project.

The Purpose of the Study should be written in a way that convinces the reader of the need to address the existing problem or gap in knowledge that the research project aims to resolve. In this section, you have to go into more detail about the benefits and value of your project for the target audience/s. 

This section features related studies and papers that will support your research topic. Use this section to analyze the results and methodologies of previous studies and address any gaps in knowledge or questions that your research project aims to answer. You may also use the data to assert the importance of conducting your research.

When choosing which papers and studies you should include in the Preliminary Literature Review, make sure to choose relevant and reliable sources. Reliable sources include academic journals, credible news outlets, government websites, and others. Also, take note of the authors for the papers as you will need to cite them in the References section.

Simply state the main objectives that your research is trying to achieve. The objectives should be able to indicate the direction of the study for both the reader and the researcher. As with other elements in the paper, the objectives should be specific and clearly defined.

Gather the research questions and equivalent research hypotheses you formulated in the earlier step and list them down in this section.

In this section, you should be able to guide the reader through the process of how you will conduct the research project. Make sure to state the purpose for each step of the process, as well as the type of data to be collected and the target population.

Depending on the nature of your research project, the length of the entire process can vary significantly. What’s important is that you are able to provide a reasonable and achievable timeline for your project.

Make sure the time you will allot for each component of your research won’t be too excessive or too insufficient so that the quality of your research won’t suffer.

Ensure that you will give credit to all the authors of the sources you used in your paper. Depending on your area of study or the instructions of your professor, you may need to use a certain style of citation.

There are three main citation styles: the American Psychological Association (APA), Modern Language Association (MLA), and the Chicago style.

The APA style is mostly used for papers related to education, psychology, and the sciences. The APA citation style usually follows this format:

The MLA citation style is the format used by papers and manuscripts in disciplines related to the arts and humanities. The MLA citation style follows this format:

The Chicago citation style is usually used for papers related to business, history, and the fine arts. It follows this citation format:

This is a concept paper sample provided by Dr. Bernard Lango from the Jomo Kenyatta University of Agriculture and Technology (modified for use in this article). Simply click the link above the download the PDF file.

• Use simple, concise language. Minimize the use of flowery language and always try to use simple and easy-to-understand language. Too many technical or difficult words in your paper may alienate your readers and make your paper hard to read. 
• Choose your sources wisely. When scouring the Internet for sources to use, you should always be wary and double-check the authenticity of your source. Doing this will increase the authenticity of your research project’s claims and ensure better data gathered during the process.
• Follow the specified format, if any. Make sure to follow any specified format when writing your concept paper. This is very important, especially if you’re writing your concept paper for class. Failure to follow the format will usually result in point deductions and delays because of multiple revisions needed.
• Proofread often. Make it a point to reread different sections of your concept paper after you write them. Another way you can do this is by taking a break for a few days and then coming back to proofread your writing. You may notice certain areas you’d like to revise or mistakes you’d like to fix. Make proofreading a habit to increase the quality of your paper.

Written by Ruth Raganit

in Career and Education , Juander How

Ruth Raganit

Ruth Raganit obtained her Bachelor of Science degree in Geology from the University of the Philippines – Diliman. Her love affair with Earth sciences began when she saw a pretty rock and wondered how it came to be. She also likes playing video games, doing digital art, and reading manga.

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• Published: 22 September 2020

Transdisciplinarity as a discipline and a way of being : complementarities and creative tensions

• Cyrille Rigolot   ORCID: orcid.org/0000-0001-8316-0226 1  

Humanities and Social Sciences Communications volume  7 , Article number:  100 ( 2020 ) Cite this article

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Transdisciplinarity is generally defined by the inclusion of non-academic stakeholders in the process of knowledge production. Transdisciplinarity is a promising notion, but its ability to efficiently address the world’s most pressing issues still requires improvement. Several typologies of transdisciplinarity have been proposed, generally with a theoretical versus practical dichotomy (Mode 1/Mode 2), and effort has focused on possible linkages between different types. However, in the last two decades, transdisciplinarity has significantly matured to the extent that the classical theoretical versus practical distinction appears clearly limited. In this paper, a reframing of the debate is proposed by considering transdisciplinarity as a new discipline and as a way of being . The conception of transdisciplinarity as a discipline can be related to the recent development of the broader discipline of “integration and implementation sciences” (i2S), to which “practical” Mode 2 transdisciplinarity is a major contributor. When transdisciplinarity is considered as a way of being , it is inseparable from personal life and extends far beyond the professional activities of a researcher. To illustrate this conception, the work and life of Edgar Morin can be used as an exemplary reference in conjunction with other streams of thought, such as integral theory. Transdisciplinarity as a discipline and transdisciplinarity as a way of being have complementarities in terms of researchers’ personal dispositions and space for expression in academia. The proposed distinction also raises the question of the status of consciousness in transdisciplinary projects, which may be a fruitful controversial topic for the transdisciplinary research community.

Introduction

In the context of unprecedented worldwide crises, transdisciplinarity is increasingly mentioned as a promising way of producing knowledge and decision-making (Lang et al., 2012 ). Transdisciplinarity is often characterized by the inclusion of non-academic stakeholders in the process of knowledge production (Scholz and Steiner, 2015 ). The notion of transdisciplinarity emerged in the 1970s and developed in different streams that correspond to different communities and contrasting research practices (Klein, 2014 ). Several typologies have been proposed to characterize these different streams and their relationships. In one of the most common typologies, based on the work of Gibbons et al. ( 1994 ) in the sociology of science, Scholz and Steiner ( 2015 ) distinguish two modes of transdisciplinarity: “Mode 1” transdisciplinarity, which is mostly theoretical, is motivated by a general search for a “unity of knowledge” and corresponds to an “inner-science activity”, while “Mode 2” transdisciplinarity, which is mostly practical, is typically characterized by the inclusion of stakeholders in participatory problem-solving approaches that are applied to tangible, real-world problems (Scholz and Steiner, 2015 ). Mode 1 transdisciplinarity is typically associated with the quantum physicist Basarab Nicolescu’s proposal of a methodology based on three axioms: (1) levels of reality, (2) the principle of the hidden third, and (3) complexity. These axioms are extensively developed in the literature (Nicolescu, 2010 ; McGregor, 2015a ). In another famous typology, Max-Neef ( 2005 ) proposes distinguishing “weak transdisciplinarity”, which can be applied “following traditional methods and logic”, and “strong transdisciplinarity”, notably inspired by Nicolescu’s work, which is characterized by a specific quantum-like logic and breaks with the assumption of a single reality (Max-Neef, 2005 ). From this perspective, transdisciplinarity is more than a new discipline or a super-discipline; it is “a different manner of seeing the world [that is] more systemic and holistic” (Max-Neef, 2005 ). As a last example, Nicolescu ( 2010 ) distinguishes three forms of transdisciplinary: (1) theoretical (referring to his own work and that of his collaborator, Edgar Morin), (2) phenomenological (corresponding to Gibbon’s Mode 2), and (3) experimental (which is based on existing data in a diversity of fields, such as education, art, and literature).

Transdisciplinarity is often described as a promising notion, but its ability to efficiently address the world’s most pressing issues still requires improvement. Although several transdisciplinary projects with non-academic stakeholders have led to significant improvements in addressing important issues, many other projects have been disappointing as the benefits claimed for participation are often not realized (Frame and Brown, 2008 ). One common response to overcome these limitations is to provide a better link between different types of transdisciplinarity regardless of the typology used. For example, for Scholz and Steiner ( 2015 ), a major challenge for transdisciplinarity is to better link Mode 1 and Mode 2 as a way to maintain high quality standards and to prevent transdisciplinarity from “being increasingly used for labeling any interactions between scientists and practitioners”. For Max-Neef ( 2005 ), efforts are needed to perfect transdisciplinarity as a world vision “until the weak is absorbed and consolidated in the strong”. Nicolescu ( 2010 ) also stresses the need to acknowledge both the diversity and the unity of his three types of transdisciplinarity (theoretical, phenomenological, experimental). In line with these different calls, some approaches have been proposed to better link different types of transdisciplinarity. For example, Rigolot ( 2020 ) suggests that quantum theory can be used as a source of insight to narrow the gap between Mode 1 and Mode 2 transdisciplinarity.

In this paper, another strategy is proposed by reframing the entire debate. Each of the mentioned typologies of transdisciplinarity has important limitations, and the very idea of a typology itself has become limited. As discussed in the next section, the notion of a Mode 1 transdisciplinarity and the related “theoretical” transdisciplinarity in Nicolescu’s terms were somewhat misleading notions from the start. In contrast, Mode 2 transdisciplinarity has evolved considerably in the last two decades, particularly with regard to its openness to shared methods and theories. The hierarchy introduced by Max-Neef ( 2005 ) between weak and strong transdisciplinarity also seems questionable. To move forward, rather than proposing another typology, it might be more fruitful to engage a dialog between transdisciplinarity as a new discipline and as a way of being . The next section presents the emergence and main characteristics of both the discipline and the way of being . Transdisciplinarity as a discipline can be seen as emerging from “Mode 2” transdisciplinarity as a result of a “bottom-up” mutualization of methodologies and theories. As an exemplary illustration, it can be related to the recent stimulating development of “integration and implementation sciences” (i2S) (Bammer, 2017 ; Bammer et al., 2020 ), although the correspondence is not exact (i2S is larger than transdisciplinarity as a discipline). Insights from complex thought (Morin, 2008 ) and integral theory (Wilber, 1995 ; Esbjörn-Hargens, 2009 ) are used to illustrate transdisciplinarity as a way of being . The third section of this paper presents the complementarities and creative tensions between a transdisciplinary discipline and a way of being before concluding with the added value of the proposed approach.

Mode 2 transdisciplinarity and the discipline of “integration and implementation sciences”

The emergence of a new academic discipline requires a broad research community with a common purpose that collaborates not only on a practical level but also on methodological and theoretical levels. Following this approach, transdisciplinarity as a discipline can be understood in terms of Mode 2 transdisciplinarity and insights from integration and implementation sciences. The notion of Mode 2 transdisciplinarity was adopted in the Zürich congress in 2000 by the major academic transdisciplinarity research community, which ultimately became the Swiss-based TD-net Network for Transdisciplinarity Research (McGregor, 2015a ). The “Zürich approach” discarded the notion of transdisciplinary as a methodology with axioms, as proposed by Nicolescu, which was later labeled “Mode 1” (Scholz and Steiner, 2015 ) or “theoretical” (Nicolescu, 2010 ) transdisciplinarity. According to Klein ( 2014 ), the Zürich congress 2000 was a pivotal event in the evolution of transdisciplinarity discourses. Originally, Mode 2 science was characterized by six principles (Gibbons et al. 1994 ) that would later be used as a basis for an “ideal-type” Mode 2 transdisciplinarity (Scholtz and Steiner, 2015 ): (1) Mode 2 knowledge is produced in the context where it will be applied; (2) it has its own distinct characteristics beyond disciplinary knowledge; (3) Mode 2 is heterogeneous in terms of skills, viewpoints and participants’ experiences; (4) structures are seen as transient and evolving rather than rigidly hierarchical; (5) the resulting knowledge is socially robust and relevant for the actors involved; (6) the quality of the produced knowledge is ensured by adequate criteria and procedures (McGregor, 2015a ). Following the principles of Mode 2, Scholtz and Steiner ( 2015 ) identified a possible “kernel” of transdisciplinary processes, which can be seen as a common purpose for the related community, in “the mutual learning among scientists and practitioners about a complex, societally relevant problem”.

As Mode 2 transdisciplinarity emerged at the expense of the methodology proposed by Nicolescu ( 2010 ), it became characterized by the adjective “practical” by contrast. Because the Zürich approach refused to embrace an overarching methodology (i.e., Nicolescu’s methodology), it became associated with “the refusal to formulate any methodology” (Nicolescu, 2010 ) and, correlatively, with an aversion to theoretical developments. However, recent breakthroughs have led to a move beyond what now appears as an over-simplification, as exemplified by the development of a new discipline of integration and implementation sciences (I2S) (Bammer, 2017 ). Integration and implementation sciences (i2S) does not strictly correspond to transdisciplinarity as it encompasses many other approaches, such as system dynamics, sustainability sciences and action research (Bammer, 2017 ). However, there is a significant overlap, as indicated in the definition of i2S as “a new discipline providing concepts and methods for conducting research on complex, real-world problems” (Bammer, 2017 ). In particular, the domain of application of i2S includes topics such as the synthesis of disciplinary and stakeholder knowledge, the understanding and management of diverse unknowns and the provision of integrated research support for policy and practice change (Bammer, 2017 ). As noted by Bammer ( 2017 ), the development of the i2S discipline was motivated by the difficulty of interdisciplinarity (including transdisciplinarity) in fitting into the mainstream and the fragmentation of methods and academic communities, which led to extensive “reinventing of methods”. A major advance has been to build a methods repository, which is also open to theoretical exchanges and development (Bammer et al., 2020 ). In a post on the i2S blog Footnote 1 presenting discussions held at the 2015 TD-net conference, a group of researchers discuss the role of theory specifically for transdisciplinary research. For this group, “theory makes clear what transdisciplinary researchers value and stand for”, which is why they feel “a responsibility to build and articulate it”. This group also insists on the specificities of transdisciplinarity research and the importance of “holding theory lightly and approaching and using it pragmatically”. While the distance from Nicolescu’s overarching approach clearly remains, such recent reflections unambiguously break with the previous view of a mostly practical transdisciplinarity that is methodology and theory averse.

Transdisciplinarity as a way of being

To date, most academic debates about types of transdisciplinarity have focused on the Mode 2 or Zürich transdisciplinarity approach, on the one hand, and the theoretical work of the quantum physicist Nicolescu ( 2010 ), on the other hand (Scholtz and Steiner, 2015 ; Bernstein, 2015 ; McGregor, 2015a ). Although these debates have yielded stimulating insights regarding, for example, the complementarity of Mode 2 transdisciplinarity with Nicolescu’s axioms, they may have reached a limit. In particular, Nicolescu’s propensity for theoretical developments and his background as a quantum physicist have contributed to the idea of a “theoretical” transdisciplinarity, as he labels it, and even further to a Mode 1 transdisciplinarity, typically associated with the image of the “ivory tower” (Scholtz and Steiner, 2015 ). To move the debate forward, the work of the French philosopher Edgar Morin can be used as a key reference for further exploration. Morin’s work and “complex thought” are widely acknowledged as a major contribution to domains such as philosophy, sociology and biology but, surprisingly, to a lesser degree to transdisciplinarity (compared to Nicolescu). However, Morin is a cosignatory with Nicolescu of the seminal “charter of transdisciplinarity” (Nicolescu et al., 1994 ). Morin himself did not engage in academic debates about transdisciplinarity as Nicolescu did (which is indicative of Morin’s approach to transdisciplinarity as a way of being ). As summarized by Montuori ( 2013 ), “ Morin’s work does not come from an attempt to escape life for an ivory tower (…) but from an effort to immerse himself in it more deeply ”. As several other commentators have noted, Montuori ( 2013 ) shows how Morin’s transdisciplinary work and well-known “complex thought” are deeply integrated with his own life experiences, including events such as the death of his mother and his participation in French resistance, about which Morin constantly reflects in journals and autobiographies. Morin is also deeply engaged in the public and political debate in France. He played a significant role, for example, in the emergence of ecological questions in the public debate (Morin and Kern, 1993 ). For Montuori ( 2013 ), Morin’s transdisciplinary approach “ does not seek to simply solve a problem, but is rather a quest for meaning derived from personal experience ”.

From his own life experiences (such as the lies around his mother’s death when he was a child and his disillusionment with the French communist party), Edgar Morin developed a particularly strong sense of distrust towards self-deception and illusion. He became aware (and then theorized) that every form of knowledge is a construction resulting from specific sources and choices that themselves depend on historical contingencies and personal preferences (Morin, 2008 ). Consequently, transdisciplinarity as a way of being cannot be fairly represented by the biased perception of only one key author, including Edgar Morin. For Gidley ( 2016 ), a diversity of authors and research fields are complementary to Morin’s way of thinking. For example, integral theory shows particularly stimulating complementarities (Gidley, 2016 ; Kelly, 2018 ). In line with the search for a unity of knowledge in Morin’s and Nicolescu’s works (Klein, 2014 ), integral theory is an attempt “to integrate as many approaches, theories and thinkers as possible in a common framework” (Esbjörn-Hargens, 2009 ). On the basis of the philosopher Ken Wilber’s seminal work ( 1995 ), integral theory has been presented as a “theory of everything” that aims to gather “separate paradigms into an interrelated network of approaches that are mutually enriching” (Esbjörn-Hargens, 2009 ). Among other authors (e.g., Gidley, 2016 ; Kelly, 2018 ), Sue McGregor ( 2015b ) has identified some strong complementarities between integral theory and transdisciplinarity, for example, with regard to the consideration of different levels of reality. For this author, integral theory can be seen as an “internal life- and world-processing orientation” (McGregor, 2015b ), which precisely corresponds to the broad definition of transdisciplinarity as a way of being adopted in the present paper. A stimulating complementarity between Integral theory and Edgar Morin’s complex thought lies in the integration of spiritual knowledge: whereas integral theory insists that there is some truth everywhere and gives strong credit to religions as holders of truth, Morin is open to spiritual knowledge but is also constantly skeptical (Montuori, 2013 ; Kelly, 2018 ). This skepticism is related to Morin embodied distrust towards self-deception, errors and illusions, which he sees constantly in knowledge production, including in the realm of science (Montuori, 2013 ; Kelly, 2018 ).

Complementarities and creative tensions

Some important characteristics of the i2S discipline were developed by Bammer et al. ( 2020 ) and can be used as a basis for characterizing transdisciplinarity as a discipline (although the i2S discipline is larger) in comparison with transdisciplinarity as a way of being . When transdisciplinarity is seen as a discipline (as part of i2S), it applies to particular issues or “wicked” problems (Bammer et al., 2020 ). More precisely, expertize in integration and implementation is required at different stages of the problem-solving process, from delimiting the problem to accommodating solutions. Bammer et al. ( 2020 ) also identify different realms where expertize can be found, which are related to communities of professional scientists or associated with academic research projects or research domains (such as unknowns and innovation). From the explorations of Bammer et al. ( 2020 ), it appears that the production of specific knowledge for the discipline of integration and implementation sciences occurs primarily in a community of professional scientists. On the other hand, from the perspective of a transdisciplinary way of being , every problem in real life can be framed as complex (Morin, 2008 ). Moreover, the relevant skills, knowledge and know-how to overcome such complex problems have been developed from ancient times and far beyond academia (Wilber, 1995 ). The way of being lens is also useful to make sense of why the first main practical application domain of Morin’s complex thought was education (Morin, 2002 ; Gidley, 2016 ). Transdisciplinarity as a new discipline and transdisciplinarity as a way of being partly overlap. Notably, the transdisciplinary way of being provides relevant “dispositions” to engage in the transdisciplinary discipline (McGregor, 2015b ). For example, participation in the public debate (agora) can be seen both as a possible characteristic of a transdisciplinary way of being (as exemplified by Edgar Morin) and as essential for the contextualization of problems in research projects (McGregor, 2015b ). Reciprocally, a transdisciplinary discipline provides specific skills and a much-needed space for the expression of the transdisciplinary way of being in academia (Ross and Mitchell, 2018 ).

However, tensions may also occur between transdisciplinarity as a discipline and as a way of being . In particular, this distinction raises the question of the status of consciousness in transdisciplinary research projects. In line with the developmental approach of the psychologist and epistemologist Jean Piaget, who coined the term transdisciplinarity (Nicolescu, 2010 ), a transdisciplinary way of being is embedded in an evolutionary approach to consciousness. A typical expression of Edgar Morin is that “we are at the prehistory of the human mind”, meaning that much of the human mental capacity remains to be explored. To a large extent, Morin’s approach is consistent with the deep exploration of transpersonal psychology by integral scholars (Gidley, 2016 ; Kelly, 2018 ). Transpersonal psychology refers to the integration of the spiritual and transcendent aspects of the human experience with the framework of modern psychology. The transpersonal is defined as “experiences in which the sense of identity or self extends beyond the individual or personal to encompass wider aspects of humankind, life, psyche or cosmos” (Walsh and Vaughan, 1993 ). Correlative with this conception, in the current context of worldwide unprecedented crisis, the transdisciplinary way of being encourages consideration of ideas such as a whole civilization change (Morin, 2011 ) based on an evolution of human thought or consciousness (Botta, 2019 ). However, many transdisciplinary scholars may hesitate to consider these ideas in the solution space of research projects. In particular, the potential tension is apparent in relation to integral theory, which explicitly and significantly includes spiritual knowledge and often associates an evolution of consciousness with processes of “awakening” (Wilber, 1995 ). Although integral theory is currently used by a large number of transdisciplinary scholars (Esbjörn-Hargens, 2009 ), it may be considered by other transdisciplinary scholars to be non-scientific and misleading. This tension is mostly implicit and seldom discussed in the literature, but it can manifest concretely as part of transdisciplinary research projects. Tension can particularly occur between a search for consensus that integrates and respects diverse stakeholders’ viewpoints as they are and the aim of transforming ways of thinking (including those of scientists themselves). In the first case, transdisciplinarity (as a discipline) is a means by which scientists contribute to problem solving. In the second case, transdisciplinarity (as a way of being ) is also a solution that must be enhanced in society at large.

Transdisciplinarity is a promising notion, but its ability to efficiently address the world’s most pressing issues has been intensively debated. To date, most debates have been structured by identifying several types of transdisciplinarity, generally with a theoretical versus practical dichotomy, and their possible linkages. In the last two decades, important efforts to mutualize methodologies and theories have led to the emergence of a discipline of integration and implementation, which enables the conception of transdisciplinarity as a discipline. Somewhat paradoxically, such a discipline seems to emerge from “Mode 2” transdisciplinarity as a result of a “bottom-up” mutualization rather than from the so-called Mode 1 “inner-science” transdisciplinarity. This distinction shows the interpenetration of Mode 2 and Mode 1 transdisciplinarity and the limits of existing typologies of transdisciplinarity. On the other hand, when transdisciplinarity is taken as a way of being , the need for knowledge and know-how for integration and implementation extends far beyond the scope of research projects and appears constantly and ubiquitously in real life. The relevant resources can be found not only in academia but also in domains such as literature and religion, keeping in mind the constant risks of errors and illusion (including in science itself). Compared to existing typologies, the consideration of transdisciplinarity as a discipline and a way of being could generate new insights in the ongoing debate about the potential and effectiveness of transdisciplinary approaches. Complementarities can be considered in terms of personal dispositions for the discipline and of a space for expression for the way of being in academia. The proposed reframing also sheds light on the status of consciousness in transdisciplinary research projects. In a sense, consciousness can be seen as a critical “unknown” for the activity of integration and implementation and a major topic for further investigation.

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Acknowledgements

This paper was funded by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25). This paper has benefited from discussions with Isabelle Arpin, Cécile Barnaud, Gaël Plumecocq, and INRAE ACT division (Sciences for Action and Transitions).

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Rigolot, C. Transdisciplinarity as a discipline and a way of being : complementarities and creative tensions. Humanit Soc Sci Commun 7 , 100 (2020). https://doi.org/10.1057/s41599-020-00598-5

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• Robert C. Scharff
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Getting to the heart of transformation

• Coleen Vogel
• Karen O’Brien

Sustainability Science (2022)

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Writing discipline specific research papers

• 1 Writing and Research in the Disciplines
• 2.1 How to Take Notes
• 2.2 How to Summarize
• 2.3 How to Paraphrase
• 2.4 How to Organize
• 2.5 How to Create a Thesis
• 2.6 How to illustrate with the second degree
• 3.1 Reliable and Unreliable Sources
• 3.2 Biology
• 3.3 Search Engines for Business Related Disciplines
• 3.4 Business Journals
• 3.5 Advertising Techniques
• 4.1 Citing Sources
• 4.2 Formatting
• 4.4 Citing In-Text
• 5.1 Bibliography
• 5.2 In-Text citation
• 5.3 Footnotes
• 5.4 Endnotes

Writing and Research in the Disciplines

This is a resource for students who are writing research projects in various disciplines. The sections that follow contain information about writing conventions in major citation style guides, sources for information in different fields of study, and helpful advice for developing a thesis and organizing material when writing a research paper.

How to Take Notes

When researching for a paper, taking notes is an essential part to the paper’s actual success. It is also a great way to avoid plagiarism and get as much original information into the paper as possible.

To start out, 3 x 5 note cards are great sources to write on, but regular loose leaf paper could be used just as well. It’s good to write down points, but summarizing and paraphrasing can be good too. I find that the best way is to take in a couple of sentences at a time. Read the small bit of information and then from what you can recollect in your mind without looking at the source, write down what you remember. This way you can avoid copying word for word and in the end avoid reiterating that exact information of ideas or actual words in your paper.

Make sure that you write down the major issues; you can go into details later. In order to take advantage of the time in which you are given to take notes, start with bold prints or look for main subject words, and then if you have time later, go back and highlight the specifics you might have missed to begin with. Also before you begin taking notes, you might want to start out with a source card so that you can go back and reference and cite your information later. Number the source cards according to the information contained in the note cards.

Melissa Collins' Work Cited Page

Brizee, H. Allen. Writing a Research Paper . 1995-2004: Internet. http://owl.english.purdue.edu/workshops/hypertext/ResearchW/notes.html Apr 11, 2008.

How to Summarize

Summarizing is one of the most important tools needed in education. It is the basis for interpreting and translating text into one’s own words. Summarizing is taking the main idea of a text and putting it in fewer words to get the general idea of the material (Jones). When we summarize, we extract what the most important parts are to show the main essence of the material (Jones). There are some general ideas and guidelines that many agree on when teaching to summarize.

Some useful ways to start the summarizing process while reading the material include underlining or highlighting important words or passages and then writing down the main ideas in a column or separate sheet of paper (“How to….”). Multiple readings of the original source are often helpful, as well as reading it quickly to only pick up the most important ideas (“How to…”). After quickly reading the material, it is helpful to go through the material more in-depth to dissect each section.

The main point to remember about a summary is that it should be put into the writer’s own words to avoid any suspicion of plagiarism. Not only is it important to not use the exact same words as the original author, but it is also important to not use the same sentence structure (“Learn to…”). To avoid exactly copying the original source, it is helpful to set the original source out of view, and then trying to write a brief overview in your own words. It is important to stay dedicated to the original source, keeping its main essence and idea, without copying it exactly (“Learn to…”).

“How To Summarize.” Mantex. 2007. Mantex. 10 April 2008. < http://www.mantex.co.uk/samples/summary.htm >.

Jones, Raymond. “Summarizing.” Reading Quest. 3 February 2007. ReadingQuest.org. 10 April 2008. < http://www.readingquest.org/strat/summarize.html >.

“Learn to Summarize.” University of Houston Victoria. 2006. Academic Center University of Houston Victoria. 10 April 2008. < http://www.uhv.edu/ac/research/write/summary.asp >.

How to Paraphrase

Paraphrasing is something that everyone should know how to do and do it correctly. Paraphrasing is often confused with summarizing. They are not the same thing and can produce very different results to the reader. To summarize something will leave only a very general idea. Paraphrasing is taking someone else’s ideas and putting them in your own, unique words.

It is important, when paraphrasing someone, to give credit to the author. If you don’t give credit where it is due, it is considered plagiarism.

Here are a few basic guidelines for solid paraphrasing from Ms. Driscoll at Purdue University: 1. Reread the original passage until you understand its full meaning. 2. Set the original aside, and write your paraphrase on a note card. 3. Jot down a few words below your paraphrase to remind you later how you envision using this material. At the top of the note card, write a key word or phrase to indicate the subject of your paraphrase. 4. Check your rendition with the original to make sure that your version accurately expresses all the essential information in a new form. 5. Use quotation marks to identify any unique term or phraseology you have borrowed exactly from the source. 6. Record the source (including the page) on your note card so that you can credit it easily if you decide to incorporate the material into your paper. (Driscoll, 1)

Mr. Plotnick gives a great example of what not to do when paraphrasing another person’s writing. Given this excerpt from Oliver Sack’s essay “An Anthropologist on Mars”:

“The cause of autism has also been a matter of dispute. Its incidence is about one in a thousand, and it occurs throughout the world, its features remarkably consistent even in extremely different cultures. It is often not recognized in the first year of life, but tends to become obvious in the second or third year. Though Asperger regarded it as a biological defect of affective contact—innate, inborn, analogous to a physical or intellectual defect—Kanner tended to view it as a psychogenic disorder, a reflection of bad parenting, and most especially of a chillingly remote, often professional, "refrigerator mother." At this time, autism was often regarded as "defensive" in nature, or confused with childhood schizophrenia. A whole generation of parents—mothers, particularly—were made to feel guilty for the autism of their children.”

Here is an example of poor paraphrasing:

“The cause of the condition autism has been disputed. It occurs in approximately one in a thousand children, and it exists in all parts of the world, its characteristics strikingly similar in vastly differing cultures. The condition is often not noticeable in the child's first year, yet it becomes more apparent as the child reaches the ages of two or three. Although Asperger saw the condition as a biological defect of the emotions that was inborn and therefore similar to a physical defect, Kanner saw it as psychological in origin, as reflecting poor parenting and particularly a frigidly distant mother. During this period, autism was often seen as a defense mechanism, or it was misdiagnosed as childhood schizophrenia. An entire generation of mothers and fathers (but especially mothers) were made to feel responsible for their offspring's autism (Sacks 247-48).” Notice how most of the sentences have only a few words rearranged or changed for synonyms. The sentence structure and paragraph layout are even almost identical to the original. Just because you cite the page numbers at the end of your paragraph doesn’t mean that you’re in the clear for plagiarism (Plotnick, 1).

Driscoll, Dana Lynn. “Paraphrase: Write it in Your Own Words.” The Owl at Purdue. 1995. University of Purdue. April 11, 2008. [1]

Plotnick, Jerry. “Paraphrase and Summary.” University College: Writing Workshop. 2007. University of Toronto. April 11, 2008. [2]

How to Organize

One of the toughest things about writing a paper is figuring out just how to organize it. Where do you begin? How do you begin? What do you include? What order do you put it in? How do you conclude your paper? Well below is a list of websites that may assist you in your organizational writing process. Included is also a brief summary of the positive and negative points of each website to help you further narrow your search.

1) How to Organize a Research Paper http://www.geocities.com/athens/oracle/4184 A Research Paper is a Tree

good: This provides a simple explanation of what to include in your paper, how to organize the basics of your paper, and what section to include specific information in. It gives an example of how an outline is a tree and how each aspect of your paper is like a specific part of the tree. This makes it easy to visually see where each piece of information should fall in your paper.

bad: It takes the example of the outline being a tree a bit far. It focuses more on making that analogy than it does presenting valuable information about writing a paper.

2) How to Organize Your Paper http://www.lasalle.edu/services/sheekey/organize.htm

good: It is a list of links to several websites to help with specific organizational problems such as having trouble putting ideas into the proper format in a paper, having trouble relating the different concepts in the paper, and having trouble getting started/getting organized.

bad: This site contains no actual information itself; it merely provides links to other sites that may be useful.

3) How to Organize a Research Paper http://www.ehow.com/how_138072_organize-research-paper.html

good: It contains a very cleat set of 8 steps to prepare for and organize your research paper. It also contains tips, warnings, and links to related articles and example research papers.

bad: It contains no examples of an actual paper, just links.

4) Research Guide for Students http://www.aresearchguide.com/1steps.html How to Write an A+ Research Paper

good: It contains a very clear list of steps to go through including choosing a topic, finding information, stating your thesis, making a tentative outline, organizing your notes, https://sites.google.com/site/bestessaywritingservicereview/ writing your first draft, and revising your final outline and draft. It includes a detailed explanation of each step as well as examples and links to find further information. It also contains a series of checklists to help ensure that you included everything and have written an effective paper.

bad: It unfortunately doesn't focus too much on actually writing the paper other than a brief discussion of the introduction, body, and conclusion.

5)How to write a Term Paper http://gale.cengage.com/free_resources/term_paper/begin.htm Begin and Organize a Research Paper

good: It contains a very detailed outline of instructions on essentially how to go about formatting your thoughts, creating a detailed outline, and different methods to implement following the creation of and outline.

bad: It only has a few examples of how to actually carry out each step of the given process which may make it a little more difficult to thoroughly follow.

How to Create a Thesis

1. Defining Your Research Topic and Starting Your Search http://www.camellia.shc.edu/literacy/tableversion/lessons/lesson3/defining2.htm

Summary: The site seemed to be a broader, not quite as in-depth, website. The main reason I found it to be a useful resource is that it provided a very helpful analogy regarding narrowing a topic. The author compared it to two lakes, a smaller lake and a larger lake. The smaller lake was deep, but the large was shallow. The point was to emphasize how if you choose the larger lake, your topic will be more vague and broad, but by choosing the smaller, deeper lake you are able to go more into detail on your topic.

2. How To Write A Thesis Statement http://www.indiana.edu/~wts/pamphlets/thesis_statement.shtml

Summary: I liked this website because it was more specific as far as discussing certain points of developing a topic which would then turn into a thesis statement. What I found most interesting is the site had two sections which provided two approaches to developing the statement depending on whether or not the topic was assigned. Despite being given a topic, one must develop their own thesis. It also gave tips on deciding upon a topic if one is not assigned. I liked where the author stated that, "In general, your thesis statement will accomplish these goals if you think of the thesis as the answer to the question your paper explores." Very well put.

3. Developing a Central Claim http://uwp.duke.edu/wstudio/documents/developing_claim_000.pdf

Summary: This Duke University Writer's Studio webpage was a useful resource for explaining the development of a research question/thesis statement. It was a bit more detailed that most of the sites I have viewed, but I believe that is a good thing. It contained a lot of information, but it was well-organized into a paragraph layout, and it was easy to read. I liked one of the points in the second paragraph stating that, "Several sentences might be necessary to convery your thesis or central claim". I think many are taught that a thesis is a single sentence, and the website addresses that appropriately.

5. Creating A Thesis Statement http://owl.english.purdue.edu/owl/resource/545/01

Summary: I was glad to find a page on the Owl site because I have found it to be a useful database for writing in the past. The design of the page is simple and clearcut, making it easy to find information about the desired subject. The webpage provides several great ideas for developing a thesis as well as provides examples. There was not as much information as I had thought there might be, but the information provided was useful and to-the-point.

How to illustrate with the second degree

The second degree can make a text attractive or pleasant, but can also introduce a malaise when not mastered (eg: to ask to keep an eye on your text to a one-eyed person).

One way to be inspired and warned is to learn the idioms , stylistic devices and other figures of speech which are typical of the destination language.

Apart from those conventional tools, it exists many different ways to build a proper style, and become proud of the communication poetic function , especially with the help of homonyms and false-friends.

How to Find Sources

Reliable and unreliable sources.

Searching for information and developing the knowledge needed to discuss your topic in depth can be the most nerve-racking step in writing a research paper. Finding credible and reliable sources of information from which to draw your conclusions has become increasingly difficult in today’s information drenched world but here are a few steps that you can take to better insure the credibility of any particular source.

Take a moment to ask yourself these questions when you are presented with a new potential source.

What is the author’s motivation for presenting the information provided in this source?

• Is it to report facts or data?
• Is the writer trying to attract attention to a cause?
• If so, what is the cause?
• Is the work geared toward advertising a product or Idea?
• Is this motivation apparent or was it hidden?
• Do any objectives of the source create a bias that is pertinent to your issue?
• Has the information presented been verified?
• How has the information been verified?
• Does the verifying party have any bias toward the issue? Do they stand to gain from the presentation of this information?

If the work has been published, check out the publishing company.

• What other works have been published by the company?
• Are the works popular? Are they scholarly? Are they accurate?

If the name of the Author is available research their previous works and background.

• Is the author experienced in the field he or she has written about?
• How has the public and/ or the academic community responded to the authors past work?

Finding reliable sources on the internet can be extremely difficult because of the publishing freedom that is found online. The site below has a lot of helpful information on determining the reliability of a web source. There is even a section that teaches you to decode a URL and discover all the information that is hidden within it

http://www.wesleyan.edu/libr/tut/websearch/evaluate.html- .

The Wikipedia policy on Reliable Sources also provides valuable guidance.

Finding resources can be overwhelming at first. With all the information that is out there, how are you supposed to know where to find all of it? The first place that you should go to is your local library and searches their online databases for the specific topic that you are working on. A great database to use in the field of biology is PubMed. [3] It has over 11 million documents, some with full text that you can view. Another very broad database is the Biological and Agricultural Index Plus. It has a great variety of full text sources that one can utilize. Another database that you can search from your own home is called EurekAlert! [4] This site is regularly updated and includes links to article and databases dealing with science and technology.

Internet sources If you want to do a simple internet search on your topic, there are a few things that you need to keep in mind. Make sure that it is a scholarly website, primarily .org or .edu. Make sure that the author is credible. If the article isn’t peer reviewed, do a background check to make sure the author is trustworthy.

If you are stuck and can’t find any sources, try checking the bibliographies of the books you already have. These usually contain relative information on the topic that you are researching and should provide a good starting point to lead you to other sources. If all else fails, just ask a librarian. They will be more than happy to assist you in finding information for a topic. Even if they don’t have the information in the library, they will either order it for you or send you to a place where you can get it.

Search Engines for Business Related Disciplines

There are a number of professional online journals that cover current business topics. These include magazines concerned with the connection between business and environment and even running businesses from home. Others include…

[5] [6] [7]

Business Journals

[8] [9] [10] [11] [12] [13]

Advertising Techniques

[14] [15] [16]

Citing Sources

Citing Sources using APA*

In APA, the works cited list at the end of a paper is called "References." The order, punctuation, and capitalization of the information in a citation are all very important, so here are general guidelines for citing sources using APA.

Listing a Publication’s Author(s) Authors are listed by last name and first (and possibly middle) initial, followed by the year of publication in parenthesis. Up to six authors may be listed in this format, placing “&” before the last author’s name. For something with more than six authors, the first six should be listed followed by “et al.” which means “and others.” If the author is unknown, the title of the publication is written before the date of publication.

Capitalization of Titles In APA format, only journal titles follow traditional capitalization techniques. For all other titles, only capitalize the first letter of the first word of a title (and subtitle), proper nouns, and the first word in a title to come after a colon or dash (excluding hyphenated compound words.)

Citing a Book Author, X. Y. (Publication Year). Title of Book: Subtitle. Publication City: Publisher. —For a book with an editor, the editor’s name is written in parentheses after the book title, followed by “Ed.” (or “Eds.”)

Citing an Article in a Periodical Author, X. Y. (Year). Title of Article. Name of Periodical. Volume(Issue). Page-Range.

—If the article being cited is a letter to the editor, this is indicated by adding “[Letter to the Editor]” between the title and the name of the periodical. —If a review is being cited, the title of the review is given followed by “[Review of the book]” and the book title.

Citing an Electronic Source Author, X. Y. (Date of Update). Title of Document on Site. In Title of Website. Retrieved Date of Access, from URLofWebsite.com —If there is no date of publication, it is replaced by “(n.d.)” —For an article from an online periodical which also appears in a printed journal, “[Electronic Version]” is added after the article title. —If the source is obtained from a university program’s website, include the program name in the “Retrieved” statement.

Article from Database Author, X. Y. (Date of Publication). Title of Article. Name of Periodical. Volume(Issue). Pages. Retrieved Date of Access, from Database Name database (Document number).

For more specific and in-depth guidelines, and to view citation examples within each category, go to http://owl.english.purdue.edu/owl/resource/560/05/

Adapted from Hacker, D. (2007). A writer's reference (6th ed.). Boston: Bedford/St. Martins. and from Neyhart, D. & Karper, E. (2008, April 9). APA formatting and style guide. In The owl at Purdue. Retrieved April 10, 2008, from http://owl.english.purdue.edu/owl/resource/560/01/

The following is a very helpful web link to a sample paper done in APA style - http://www.vanguard.edu/uploadedFiles/Faculty/DDegelman/psychapa.doc .

Also, another good reference tool is Diana Hacker’s 6th Edition of A Writer’s Reference . Pages 414 through 459 give you all sorts of advice when creating a paper to be done in APA. Another sample paper is given in pages 451 through 459 of the Hacker book.

Formatting your paper in APA style can be a little tedious, but the process is quite simple once you understand the rules. For example, you are allowed to insert visuals such as graphs, comics, or any type of pictures. Make sure each visual has a title and number (like Table 1, Table 2, Table 3, etc.) posted above the artwork and place this fragment in a paragraph that addresses its purpose.

Basic Rules:

Margins = 1 inch

Double Space? Yes and No – do double space your actual paper (this includes in-text citations), but single space footnotes.

Quotations: If you want to use a quote that is longer than 40 words, don’t use quotation marks. Instead, you need to indent the quotation 5 space bar clicks from the left margin (if this confuses you – check out a sample paper to get a more hands-on approach).

Headings: Not required, but can be very useful for addressing your main points. Center your headings.

Page Numbers: If you have a title page, label it i (where? far upper right) …if you have an abstract page, label it ii. One of the most noticeable characteristics of APA style papers are the page numbers partnered with a title. The title can be a shortened version of your main title. Here is an example:

Main Title: Bee Keeping Journals of the Late 1860’s: The Value of These Collections to American Science

Heading of page 2 in an APA type paper: Bee Keeping Journals 2

Works Cited: You’ll actually label your “Works Cited” page, as “References”. This will be the final page of your paper, one that still deserves a heading, and will be double spaced. When typing authors’ names, you will use initials instead of first names (Ex: “Mallory Durham” would be cited as “Durham, M.”). Secondly, don’t use quotations when citing article titles. Instead you will italicize .

IMPORTANT : A distinct trait of APA style reference pages is the format. Alphabetize by the first letter of authors’ last name(s) – if there is no author, then type out the source name (Ex: March for Cancer Foundation - in this case you would use the “M” from “March” to alphabetize). Don’t number your sources. The first line (starting with author name(s) or other sources) is typed with no indention, but all of the lines following the first one will be shifted 5 space bar clicks from the left margin.

Free APA bibliography generator

If you have never composed a cited bibliography in APA, MLA or CSE before, this website could be the answer to all your questions. Simply create a new folder on the website, fill in the citation type, format, author, title, publication year and a few more optional boxes, hit format and it’s completed! Another great feature about this site is that you can either print it from the website directly, or download it to another document. www.carmun.com

APA formatting

Need help formatting your paper? Learn the basics for writing an APA style research paper. It is user friendly with great links to more detailed features that go into writing a research paper. Most cites like this are not free and very hard to navigate through. The home page contains an example of a term paper with all the correct formats. However, I was still slightly confused after skimming though the given work. To fix this problem, the creators of English Works provide links to general guidelines of APA, how to avoid plagiarism, guides to writing a thesis, introduction and conclusion, prewriting strategies, and guide to paraphrasing. If you are a first time APA style writer, I highly recommend checking out this cite! http://depts.gallaudet.edu/englishworks/writing/apa_sample.html

Everything you need to know about writing an APA paper

This cite is a hypertext which is perfect for this class since we have been dealing with them! It is focused towards writing psychology papers but its main purpose is to guide another in writing and giving examples on an APA paper. Everything is listed on the helpful cite: style details, abbreviations, numbers, citations in text, quotations, title page, introduction, methods, references, the body, and conclusions. If you are still confused and want examples there is an appendix of example title pages and reference sections. http://www.uwsp.edu/PSYCH/apa4b.htm

Tips For Citing Your Academic Paper In APA Format

A brief lesson on how students and researchers can learn the basics of constructing a properly formatted paper that meets APA guidelines. https://canvas.elsevier.com/eportfolios/9904/Home/Five_Tips_For_Citing_Your_Academic_Paper_In_APA_Format

Citing In-Text

In-text Citation of Sources in APA Style

In APA style, not only direct quotes used within a paper must be cited. In addition to these, in-text citations must include all ideas borrowed from any other source. This may be paraphrased material or it may even be something like statistics. Visuals such as cartoons and graphs must also be cited. General information known by many and appearing in numerous other sources is not cited.

There is a three-part system that is accepted by the APA. First, the authors’ names are included in a “signal phrase” and the publication date in parentheses. Second, a page number in parentheses follows the cited material. Finally, the last page of the paper must be an alphabetical list of sources cited in the paper.

CMS writing style stands for the Chicago Manual of Style first used at the University of Chicago in 1890. It is known for being one of the easiest and most informative citing methods. CMS is usually used in the humanities such as art, literature, or history because of its importance placed on the author of the work instead of other details: date and placed published. Here are the rules for the CMS citation. [17]

Bibliography

1. The title is centered an inch below the top of the page

2. Citations are arranged in alphabetical order

3. Citations are double-spaced between entries, but single within the entry

4. The first line of each citation is aligned with the left margin and the subsequent lines are indented five spaces.

In-Text citation

1. All in-text citations direct the reader to the appropriate source in the Endnotes at the end of the text.

2. Endnotes are a list of the source ordered and numbered according to the sequential number of the corresponding in-text citation.

3. The first in-text citation is superscripted with a 1, the second in-text citation is superscripted with a 2 (no really), and the numbers continue on.

1. Footnotes are used to provide complete publication information for unoriginal content or structure of a text.

2. Any text, for which there is information in the Footnotes, is superscripted with a corresponding number. The number is found in the Footnotes section at the bottom of the page.

1. Endnotes are used to provide complete publication information for each unoriginal idea or concept in the writing.

2. Any text, for which there is information in the endnotes, is superscripted again with a number and that number can be found in the endnotes with the citation.

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What is a discipline? : The conceptualization of research areas and their operationalization in bibliometric research

• Björn Hammarfelt
• Published 11 September 2018

10 Citations

Field, capital, and habitus: the impact of pierre bourdieu on bibliometrics.

• Highly Influenced

Tracing the context in disciplinary classifications: A bibliometric pairwise comparison of five classifications of journals in the social sciences and humanities

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