The central role of such epistemic concepts as theory, explanation, model, or mechanism is rarely questioned in philosophy of science. Yet, what is their actual use in the practice of science? Here, we deploy text-mining methods to investigate the usage of 61 epistemic notions in a corpus of full-text articles from the biological and biomedical sciences (N = 73,771). The influence of disciplinary context is also examined by splitting the corpus into subdisciplinary clusters. The results reveal the intricate semantic networks that these concepts actually form in the scientific discourse, not always following our intuitions, at least in some parts of science.

Grant Ramsey and Michael Deem argue that appreciating the role that empathy plays in posttransgression guilt leads to a more promising account of the emotion’s evolutionary origins. But because their proposal fails to adequately distinguish guilt from shame, we cannot say which of the two emotions we are actually getting an evolutionary account of. Moreover, a closer look at the details suggests both that empathy may be more relevant for our understanding of shame’s evolutionary origins than guilt’s and that guilt is unlikely to be an adaptation.

We provide five rearticulations of the thesis that the structure of spacetime is conventional, rather than empirically determined, based upon variation of the structures that are empirically underdetermined and modal contexts in which this underdetermination occurs. Three of the five formulations of conventionalism will be found to fail. Two are found to open up new interesting problems for researchers in the foundations of general relativity. In all five cases, our analysis explores the interplay between geometric identities, symmetry, conformal structure, and the dynamical content of physical theories with the conventionalism dialectic deployed as a tool of explication, clarification, and exploration.

Basal cognition investigates cognition working upward from nonneuronal organisms. Because basal cognition is committed to empirically testable hypotheses, a methodological challenge arises: how can experiments avoid using zoocentric assumptions that ignore the ecological contexts that might elicit cognitively driven behavior in nonneuronal organisms? To meet this challenge, I articulate the principle of dynamic holism (PDH), a methodological principle for guiding research on nonneuronal cognition. I describe PDH’s relation to holistic research programs in human-focused cognitive science and psychology then present an argument from analogy based on holistic developmental biology. Last, I examine two experiments exemplifying the need for PDH.

Piccinini’s usability constraint states that physical processes must have “physically constructible manifestation[s]” to be included in epistemically useful models of physical computation. But to determine what physical processes can be implemented in physical systems (as parts of computations), we must already know what physical processes can be implemented in physical systems (as parts of processes for constructing computing systems). We need additional assumptions about what qualifies as a building process. Piccinini implicitly assumes a classical computational understanding of executable processes, but this is an assumption imposed on physical theories and may artificially limit our picture of epistemically useful physical computation.

I discuss a contemporary solution to the dynamic problem of old evidence (POE), as proposed by Sprenger. Sprenger’s solution combines the Garber–Jeffrey–Niiniluoto (GJN) approach with Howson’s suggestion of counterfactually removing the old evidence from scientists’ belief systems. I argue that in the dynamic POE, the challenge is to explain how an insight under beliefs in which the old evidence E is known increased the credence of a scientific hypothesis. Therefore, Sprenger’s counterfactual solution, in which E has been artificially removed, does not resolve the problem. I consider several potential responses.

Recent advocates of “field philosophy” make the case that philosophy “needs to get outside more often”; alongside disciplinary modes of practice we should cultivate philosophical work that is consequential in real-world, practical terms. This takes a number of different forms, many of them captured by a framework for analyzing engaged philosophy of science proposed by Plaisance and Elliott. I draw on three examples of field-engaged philosophy of science that address the legacies of settler-colonialism archaeology to illustrate the promise of field philosophy.

In a conventional experiment, scientists typically aim to learn about target systems by manipulating source systems of the same material type. In an analogue quantum simulation, by contrast, scientists typically aim to learn about target quantum systems of one material type via an experiment on a source quantum system of a different material type. In this article, we argue that such inferences can be justified by reference to source and target quantum systems being of the same empirical type. We illustrate this novel experimental practice of wavefunction engineering with reference to the example of Bose–Hubbard systems.

Sexes are normally taken to be fundamental categories in biology—many sexually reproducing organisms fall under the categories of female/male. Much research aims at explaining differences between sexes. Sexual selection forms a central framework for explaining “typical” distributions of traits among sexes and explicating circumstances leading to “reversal.” I claim that sexual selection models need not make use of sexes and that sexes lack explanatory significance in such models. I offer a framework of reproductive dimorphism and argue that it is better than that of sexes because it (1) is more compatible with explanations of sexual selection and (2) allows for their greater applicability.

Historians have shown that philosophical discussions about the implications of relativity significantly shaped the development of European philosophy of science in the 1920s. Yet little is known about American debates from this period. This article maps the first responses to Einstein’s theory in three U.S. philosophy journals and situates these papers within the local intellectual landscape. I argue that these discussions (1) stimulated the development of a distinctly American branch of philosophy of science and (2) paved the way for the logical empiricists who emigrated to the United States in the years before World War II.

I propose an account of the model-based structure of the present-day high energy physics experiments in which the relations among the theoretical, experimental and simulation models constitute a non-linear structure that is akin to a network of models (NoM). I argue that while the proposed NoM subsumes Suppes’ hierarchy of models (HoM) as the model-based characterization of the inference leading from the data to the validity or invalidity of the hypothesis tested in an experiment, it involves a model-based characterization of the inference leading from the collision of particles to the acquisition of data, which is missing in Suppes’ HoM.

Scientific disagreements are an important catalyst for scientific progress. But what happens when scientists disagree amid times of crisis, when we need quick yet reliable policy guidance? In this article, we provide a normative account for how scientists facing disagreement in the context of “fast science” should respond and how policy makers should evaluate such disagreement. Starting from an argumentative, pragma-dialectic account of scientific controversies, we argue for the importance of higher-order evidence (HOE), and we specify desiderata for scientifically relevant HOE. We use our account to analyze the controversy about the aerosol transmission of COVID-19.

In a recent article on the interpretation of probability in evidence-based medical practice, Jonathan Fuller argues that we should interpret probabilities as credences in individual cases because this avoids some important problems. In this article, I argue that Fuller misidentifies the real issue and so fails to offer a meaningful solution to it. The real problem with making probability judgments in individual cases is deciding which objective considerations ought to constrain our formation of credences. This leads us to the reference class problem, which, as Alan Hajek argues, is a problem for any interpretation of probability.

Howson (2000) contends that the No-Miracles argument fails as an argument in support of scientific realism because it commits the base-rate fallacy. Psillos (2009) has defended the argument by appealing to cases that involve conditional probabilities but where base-rate information can properly be ignored. I show that Psillos’s defense of the No-Miracles argument is inadequate and that the prospects for a purely probabilistic formulation of the argument are dim. I end by considering whether interpreting the argument as an inference to the best explanation might better serve the scientific realist, concluding that such an approach would limit the realist’s ability to effectively counter the Pessimistic Induction.

We model the impact of different incentives on journal behavior in undertaking peer review. Under one scheme, the journal aims to publish the highest-quality papers; under the second, the journal aims to maintain a high rejection rate. Under both schemes, journals prefer to set very high standards for acceptance despite allowing significant error in peer review. Under the second scheme, however, in order to encourage more submissions of mediocre papers, the journal is incentivized to make its editorial process less accurate. This leads to both worse peer review and lower-quality articles being published.

The COVID-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modelers. This raises novel questions for the philosophy of science. Here I investigate the possibility of 'natural models' in predicting the SARS-Cov-2 virus’s trajectory for epidemiological purposes. I argue that to the extent that these can be considered scientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making my case, I draw from work on both model organisms and natural experiments as well as recent work in epidemiology.

Economists often consult multiple models in order to combat model uncertainty in the face of misspecification. By examining modeling practices at the Bank of England, this paper identifies an important, but underappreciated modeling procedure. Sometimes an idealized model is manipulated to reproduce the results from another distinct auxiliary model, ones which it could not produce on its own. However, this procedure does not involve making the original model “more realistic,” insofar as this means adding in additional causal factors. This suggests that there are ways to make models more representationally adequate that do not involve de-idealization in the straightforward sense.

In discussions of the power of causal explanations, one often finds a commitment to two premises. The first is that, all else being equal, a causal explanation is powerful to the extent that it cites the full causal history of why the effect occurred. The second is that, all else being equal, causal explanations are powerful to the extent that the occurrence of a cause allows us to predict the occurrence of its effect. This article proves a representation theorem showing that there is a unique family of functions measuring a causal explanation’s power that satisfies these two premises.

Inferences from clinical research results to estimates of therapeutic effectiveness suffer due to various biases. I argue that predictions of medical effectiveness are prone to failure because current medical research overlooks the impacts of a particularly detrimental set of biases: meta-biases. Meta-biases are linked to higher-level characteristics of medical research and their effects are only observed when comparing sets of studies that share certain meta-level properties. I offer a model for correcting research results based on meta-research evidence, the bias dynamics model, which employs regularly updated empirical bias coefficients to attenuate estimates of therapeutic effectiveness.

In a recent paper Winsberg (2021) argued in favor of research into geoengineering by relying on Good’s theorem, which states that conducting research maximizes one’s expected utility. However, this result sometimes fails for risk-avoidant agents (Buchak 2010). Since risk avoidance captures some of the “precautionary” intuitions that critics of geoengineering share, it is important to see if geoengineering research would maximize one’s utility if risk avoidance is taken into account. I show that under some conditions conducting geoengineering research would not maximize risk-weighted expected utility.