The goal of this article is to analyze the role of convention in interpreting physical theories—in particular, how the distinction between the conventional and the nonconventional interacts with judgments of equivalence. We will begin with a discussion of what, if anything, distinguishes those statements of a theory that might be dubbed “conventions.” This will lead us to consider the conventions that are not themselves part of a theory’s content but are rather applied to the theory in interpreting it. Finally, we will consider the idea that what conventions to adopt might, itself, be regarded as a matter of convention.

The problem of quantity is the problem of identifying what about the physical world explains why it can be so well represented with mathematical entities. I introduce “quantitative primitivism,” the dominant position in the literature, which offers only a partial solution to the problem of quantity. I argue that a reductive account of quantitativeness provides a full solution to the problem and describe two reductive accounts in the literature. I discuss some of the unique metaphysical consequences of reductive accounts of quantity, including a novel dissolution to the long-standing absolutist–comparativist debate.

Teleparallel gravity, an empirically equivalent counterpart to general relativity, represents the influence of gravity using torsional forces. It raises questions about theory interpretation and underdetermination. To better understand the torsional forces of teleparallel gravity, we consider a context in which forces are better understood: classical spacetimes. We propose a method of incorporating torsion into the classical spacetime context that yields a classical theory of gravity with a closed temporal metric and spacetime torsion. We then prove a result analogous to the Trautman degeometrization theorem: that every model of Newton–Cartan theory gives rise, non-uniquely, to a model of this theory.

The original architects of the representational theory of measurement interpreted their formalism operationally and explicitly acknowledged that some aspects of their representations are conventional. We argue that the conventional elements of the representations afforded by the theory require careful scrutiny as one moves toward a more metaphysically robust interpretation by showing that there is a sense in which the very number system one uses to represent a physical quantity such as mass or length is conventional. This result undermines inferences which impute structure from the numerical representational structure to the quantity it is used to represent.

There is a mysterious twofold change in Du Châtelet’s position on Newtonian attraction: from acceptance thereof as an explanatory principle in Essai sur l’optique (ca. 1738–39), to rejection in the 1740 Institutions, and returning to acceptance again in her Commentary (1756) to Newton’s Principia. In this article, I suggest that we turn to the 1742 Institutions for answers. There, Du Châtelet introduces physical explanation and maintains that we can appeal to certain physical qualities (such as attraction) for explanatory force. Using this case study, I argue that the scholarship will benefit greatly from turning to the 1742 edition going forward.

I examine three arguments that purport to show that connectionist, associationist architectures cannot achieve key features of human thought. Hume anticipated each of these three arguments and provided a unified strategy for responding to each, the “externalist gambit.” On this account, external natural language provides the necessary structure for associationist systems to achieve those features of thought that their opponents take them to lack. The externalist gambit provides a promising avenue for today’s defenders of connectionism about the human mind.

Epistemic stances are collections of attitudes, values, aims, and policies relevant to assessing evidence, eventuating in belief or agnosticism regarding the output of scientific investigations. If, in some cases, conflicting stances promoting scientific realism and antirealism, respectively, are rationally permissible, this would seem to undermine the possibility of resolving certain debates between realists and antirealists. In this article I reply to two concerns about this conception of stances, to the effect that: (1) a stance underlying realism is, in fact, rationally obligatory for realists, given certain natural assumptions; and (2) this sort of permissivism would validate pseudoscience and science denialism.

This article introduces the snap hook methodology, a method used notably in astrochemistry as a way to indirectly validate and assess the accuracy of computational calculations in the absence of experimental or observational data. We argue that this methodology has tremendous potential for all computationally intensive scientific fields as a substitute for traditional verification and validation standards when those are not accessible and estimating the reliability of numerical predictions becomes a real difficulty. The goal of this article is to give to this method, which seems to be implicitly relied upon in many areas, a proper formulation, in order for philosophers of science to enter the debate and to highlight its undeniable potential in terms of interdisciplinary facilitation and knowledge transmission.

An influential heuristic for thinking about climate adaptation asserts that “natural” adaptation strategies are the best ones. This heuristic has been roundly criticized but is difficult to dislodge in the absence of an alternative. We introduce a new heuristic that assesses adaptation strategies by looking at their maturity, power, and commitment. Maturity is the extent to which we understand an adaptation strategy’s effects. Power is the size of the effect an adaptation strategy will have. Commitment is the degree to which an adaptation strategy is difficult to test or reverse.

Biomedical deployments of data science capitalize on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modeling practices, particularly the recognition of transdisciplinary expertise as crucial to developing and interpreting data models. Acknowledging such shifts in evidential, experimental, and modeling practices helps avoid harmful applications of data-intensive methods.

“Actionability” is a key concept in precision oncology. Its precise definition, however, remains contested. This article undertakes a philosophical analysis of “actionability” to aid in conceptual clarification. We map distinct concepts of actionability, arguing that each is best understood as a contextually objective category articulated to mitigate risk of “conceptual slippage.” We defend “interactive pluralism,” acknowledging the need for distinct concepts but also for conceptual interaction in practice. This article thus offers insights for both practitioners and philosophers, clarifying approaches to actionability for scientists and clinicians and serving as a case study to test competing views on scientific pluralism.

In recent debates about the replication crisis, two positions have been dominant: one that focuses on methodological reforms and one that focuses on theory building. This paper takes up the suggestion that there might be a deeper difference in play, concerning the ways the very subject matter of psychology is construed by opposing camps, i.e., in terms of stable effects versus in terms of complexity. I argue that each gets something right, but neither is sufficient. My analysis suggests that the context sensitivity of the psychological subject matter needs to be front and center of methodological and theoretical efforts.

The “psychologist’s green thumb” refers to the argument that an experimenter needs an indeterminate set of skills to successfully replicate an effect. This argument is sometimes invoked by psychological researchers to explain away failures of independent replication attempts of their work. In this article, I assess the psychologist’s green thumb as a candidate explanation for individual replication failure and argue that it is potentially costly for psychology as a field. I also present other, more likely reasons for these replication failures. I conclude that appealing to a psychologist’s green thumb is not a convincing explanation for replication failure.

Recent proposals that the substrate of memory is molecular raise questions about where this molecular model stands in relation to the dominant synaptic model of memory. In this article, we address the perceived rivalry between these models and ask whether they can be integrated. We argue that addressing rivalry or integration requires delineating the explananda of synaptic and molecular models, as well as revisiting assumptions about how these models account for their explananda. The perceived rivalry between these models exemplifies epistemic costs that arise when we try to pit explanatory models as rivals or integrate them.

We propose a novel account of evolutionary transitions in individuality as life cycle closure: that is, the emergence of a new embedding life cycle. To characterize this process, we show how the life trajectory of lower-level entities (e.g., cells) can be coarse-grained into classes of a higher-level entity. We argue that only higher-level entities displaying two necessary conditions for the existence of a life cycle (e.g., multicellular organisms) have achieved life cycle closure. Throughout, we illustrate our point with stage-structured demographic models that yield a rigorous characterization of the conditions for life cycle closure.

I characterize a role for “environments” as ecological scaffolding of organism development in the evolution of novelty. I interpret Rainey’s bacterial experimental system for empirically modeling evolutionary transition to multicellularity as an ecological-developmental problem in terms of a formal model of Kauffman’s concept of evolution into the “adjacent possible.” I propose a scenario to interpret scaffolds dynamically, treating them as organisms modeled in the same way as the developing systems they scaffold, rather than as fixed constraint boundary conditions. The scenario suggests avenues for mathematically modeling scaffolding dynamics.

The founder of conservation biology, Michael Soulé, set out a vision for conservation biology that was explicitly value-laden, analogous to cancer-biology. In so doing, he drew on the writings of Aldo Leopold, known among philosophers primarily for his land ethic. Employing and extending the work of Anderson (2004) and Clough (2020), I argue that the Leopoldian views that Soulé was drawing on were the product of the coevolution of descriptive and evaluative beliefs over the course of Leopold’s life, grounded in his experiences, resulting in tested and reliable—albeit defeasible—values underlying conservation biology.

Fitness has taken center stage in debates concerning how best to identify evolutionary transitions in individuality (ETIs). An influential framework proposes that an ETI occurs only when fitness is exported from constituent particles to a collective. We reformulate the conceptual structure of this framework as involving three steps. The culminating step compares “counterfactual” fitnesses against a long-run measure of fitness. This comparison assumes that collective-level fitness mereologically supervenes on particle fitness. However, if this assumption is rigorously enforced, the proposed conditions for identifying ETIs prove to be too weak. We here suggest an alternative model of ETIs centered around traits.

A long-standing problem in understanding goal-directed systems has been the insufficiency of mechanistic explanations to make sense of them. This article offers a solution to this problem. It begins by observing the limitations of mechanistic decompositions when it comes to understanding physical fields. We argue that introducing the field concept, as it has been developed in field theory, alongside mechanisms is able to provide an account of goal directedness in the sciences.

I argue that machine learning (ML) models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.