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Scientific research is constrained by limited resources, so it is imperative that it be conducted efficiently. This paper introduces the notion of epistemic expression, a kind of representation that expedites the solution of research problems. Epistemic expressions are representations that (i) contain information in a way that enables more reliable information to place the most stringent constraints on possible solutions and (ii) make new information readily extractible by biasing the search through that space. I illustrate these conditions using historical and contemporary examples of biomolecular structure determination. Then, I argue that the notion of epistemic expression parts ways with pragmatic accounts of scientific representation and an understanding of models as artifacts, neither of which require models to accurately represent. Explicating epistemic expression thus fills a gap in our understanding of scientific practice, extending Morrison and Morgan's (1999) conception of models as investigative instruments.

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Persuasive arguments for using theory have been influential in health behavior and health promotion research. The use of theory is expected to improve intervention outcomes and facilitate scientific advancement. However, current empirical evaluations of the benefits of theory have not consistently demonstrated strong effects. A lack of resolution on this matter can be attributed to several features of the current body of evidence. First, the use of theory may be confounded with other features that impact health-related outcomes. Second, measurement of theory use has not been reliable. Third, the field conflates models and theories. Lastly, the evidentiary status and applicability of theories are not considered. Addressing these challenges during the execution of meta-analyses and designing original research specifically to estimate the benefits of theory could improve research and practice.

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This paper proposes an account of accurate scientific representation in terms of techniques that produce data from a target phenomenon. I consider an approach to accurate representation that abstracts from such epistemic factors, justified by a thesis I call Ontic Priority. This holds that criteria for representational accuracy depend on a pre-established account of the nature of the relation between a model and its target phenomenon. I challenge Ontic Priority, drawing on the observation that many working scientists do not have access to information allowing them to describe this relation between model and target or compare them accordingly. I critique the ability of an ontic-first approach to provide accuracy criteria in such cases and present historical support for an alternative according to which a model is accurate if and only if integrating the model into a theory of the data acquisition process yields well-fitting predictions of patterns in the data.

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Previously, I (Boesch 2017) described a notion called "representational licensing"-the set of activities of scientific practice by which scientists establish the intended representational use of a vehicle. In this essay, I expand and develop this concept of representational licensing. I begin by showing how the concept is of value for both pragmatic and substantive approaches to scientific representation. Then, through the examination of a case study of the Mississippi River Basin Model, I point out and explain some of the activities of representational licensing that help to establish the representational nature of this model. Throughout the exploration of the case study, I pause to identify some important lessons which apply more generally about the nature of representational licensing in science.

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Idealization is commonly understood as distortion: representing things differently than how they actually are. In this paper, we outline an alternative artifactual approach that does not make misrepresentation central for the analysis of idealization. We examine the contrast between the Hodgkin-Huxley (1952a, b, c) and the Heimburg-Jackson (2005, 2006) models of the nerve impulse from the artifactual perspective, and argue that, since the two models draw upon different epistemic resources and research programs, it is often difficult to tell which features of a system the central assumptions involved are supposed to distort. Many idealizations are holistic in nature. They cannot be locally undone without dismantling the model, as they occupy a central position in the entire research program. Nor is their holistic character mainly related to the use of mathematical and statistical modeling techniques as portrayed by Rice (2018, 2019). We suggest that holistic idealizations are implicit theoretical and representational assumptions that can only be understood in relation to the conceptual and representational tools exploited in modeling and experimental practices. Such holistic idealizations play a pivotal role not just in individual models, but also in defining research programs.

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Inferentialists about scientific representation hold that an apparatus's representing a target system consists in the apparatus allowing "surrogative inferences" about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting my own solution. I develop an analogy to exploitable glitches in a game. Even though inconsistent representational apparatuses may in some sense allow for contradictions to be generated within them, doing so violates the intended function of the apparatus's parts and hence violates representational "gameplay".

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Science textbooks communicate fundamental discoveries and serve as platforms showcasing role models for students. However, the scientists represented across undergraduate textbooks do not reflect the demographic makeup of the student population reading those materials. We recommend a series of changes within curricula to challenge the stereotypical identity of science.

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It is commonly held that in vivo biological experimental models are concrete and non-fictional. This belief is primarily supported by the fact that in vivo studies involve biological models which are alive, and what is alive cannot be fictional. However, I argue that this is not always the case. The design of an experimental model could still render an in vivo model fictional because fictional elements and processes can be built into these in vivo experimental models. These fictional elements are essential parts of a credentialed fiction because the designs of in vivo experimental models are constrained by imaginability, conceivability, and credit-worthiness. Therefore, despite its fictionality, it is credible for an in vivo experimental model to stand in for the phenomenon of interest.

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Mechanism diagrams exhibit visually the organized parts and operations of a biological mechanism. A mechanism diagram can facilitate mechanistic research by providing a mechanistic explanation of the phenomenon of interest. Much research has been focusing on the mechanistic explanation and the explanatory mechanistic models. As a specific type of scientific diagram, a simple mechanism diagram can be explanatory by drawing on the rich explanatory resources of non-depicted background knowledge. The relationship between the visually depicted and the background knowledge is underexplored. It is unclear how the non-depicted background knowledge of a mechanism diagram contributes to providing a better-informed explanation of the phenomenon of interest in biological sciences. With the aim to explore this relationship, I articulate that a mechanism diagram provides a mechanistic explanation by a process called abstraction-by-aggregation. Through visual cues, the unified relevant background knowledge provides an epistemic access to a better-informed explanation.

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It is a widespread assumption in philosophy of science that representations of data are not explanatory-that they are mere stepping stones towards an explanation, such as a representation of a mechanism. I draw on instances of representational and explanatory practice from mammalian chronobiology to suggest that this assumption is unsustainable. In many instances, biologists employ representations of data in explanatory ways that are not reducible to constraints on or evidence for representations of mechanisms. Data graphs are used to represent relationships between quantities across conditions, and often these representations are necessary for explaining particular aspects of the phenomena under study. The benefit of the analysis is two-fold. First, it provides a more accurate account of explanatory practice in broadly mechanistic investigation in biology. Second, it suggests that there is not an explanatorily "fundamental" type of representation in biology. Rather, the practice of explanation consists in the construction of different types of representations and their employment for distinct explanatory purposes.

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