Scientists may sometimes generalize from their samples to broader populations when they have not yet sufficiently supported this generalization. Do such hasty generalizations also occur in experimental philosophy? To check, we analyzed 171 experimental philosophy studies published between 2017 and 2023. We found that most studies tested only Western populations but generalized beyond them without justification. There was also no evidence that studies with broader conclusions had larger, more diverse samples, but they nonetheless had higher citation impact. Our analyses reveal important methodological limitations of many experimental philosophy studies and suggest that philosophical training may not protect against hasty generalizations.

Replacing the value-free ideal (VFI) for science requires attention to the broader understanding of how science in society should function. In public spaces, science needed to project the VFI in norms for science advising, science education, and science communication. This resulted in the independent science advisor model and a focus on science literacy for science education and communication. Attending to these broader implications of the VFI that structure science and society relationships is crucial if we are to properly replace the VFI with a better ideal.

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.

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.

The generalized selected effects theory of function (GSE) holds that a trait’s proper function is an activity that historically caused its differential persistence or differential reproduction within a population, construed as a collection of individuals that impact each other’s persistence or reproduction chances. Several critics have taken aim at GSE on the grounds that its appeal to populations is either unfit for purpose or arbitrary. Here I revise GSE by articulating a notion of population that is fit for purpose and showing that its selection is not arbitrary but flows from the realist commitments of the selected effects theory.

I argue that there are two distinct approaches to understanding reduction: the ontology-first approach and the theory-first approach. They concern the relation between ontological reduction and inter-theoretic reduction. Further, I argue for the significance of this distinction by demonstrating that either one or the other approach has been taken as an implicit assumption in, and has in fact shaped, our understanding of what statistical mechanics is. More specifically, I argue that Boltzmannian statistical mechanics assumes and relies on the ontology-first approach, whereas Gibbsian statistical mechanics should assume the theory-first approach.

Modal relationism is the view that our best physical theories can dispense with substantival space or spacetime in favor of possible configurations of particles. Kenneth Manders argues that the substantivalist conception is equivalent to this Leibnizian conception of space. To do so, Manders provides a translation $f$ from the Newtonian theory ${T_N}$ into the Leibnizian modal relationist account ${T_L}$. In this article, we show that the translation does not establish equivalence because there is no translation $g:{T_L} \mapsto {T_N}$ that preserves theoremhood. This seems to show that the modal relationist theory ${T_L}$ is less parsimonious than its substantivalist rival.

Traditionally, the debate about health and disease is characterized as an opposition between naturalism and normativism. However, recent contributions show that theories of health and disease need not be purely naturalistic or normative but may be located somewhere in between. The first purpose of this article is to further advance this line of nuancing. The second purpose is to argue in favor of a specific position, which the added nuances reveal. I call this position subjectively salient naturalism. If one is interested in scientific concepts of health and disease, subjectively salient naturalism is a more plausible position than naturalism.

The demise of the value-free ideal constitutes a threat to public trust in science. One proposal is that, whenever making value judgments, scientists rely only on democratic values. Because the influence of democratic values on scientific claims and recommendations is legitimate, public trust in science is warranted. I challenge this proposal. Appealing to democratic values will not suffice to secure trust because of at least two obstacles: polarization and marginalization.

Cognitive representations are typically analyzed in terms of content, vehicle, and format. Although current work on formats appeals to intuitions about external representations, such as words and maps, in this article, we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, that is, the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, makes space for a variety of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence.

The different social institutions involved in the development and distribution of science and technology tend to promote, primarily, commercial and private interests, even in situations with high social stakes. Acknowledging how these interests interplay with social and epistemic values is fundamental for understanding the role of values in science today. To show this, I use the global pandemic and the race to develop and distribute vaccines against COVID-19 as an example. I argue that a mere circumstantial alignment of values across the social institutions of science is not enough and that instead we need a more substantive alignment.

Scientific hedges are communicative devices used to qualify and weaken scientific claims. Gregor Betz has argued—unconvincingly, we think—that hedging can rescue the value-free ideal for science. Nevertheless, Betz is onto something when he suggests there are political principles that recommend scientists hedge public-facing claims. In this article, we recast this suggestion using the notion of public justification. We formulate and reject a Rawlsian argument that locates the justification for hedging in its ability to forge consensus. On our alternative proposal, hedging is often justified because it renders scientific claims as publicly accessible reasons.

Epistemic uncertainties are included in probabilistic risk assessment (PRA) as second-order probabilities that represent the degrees of belief of the scientists that a model is correct. In this article, I propose an alternative approach that incorporates the scientist’s confidence in a probability set for a given quantity. First, I give some arguments against the use of precise probabilities to estimate scientific uncertainty in risk analysis. I then extend the “confidence approach” developed by Brian Hill and Richard Bradley to PRA. Finally, I claim that this approach represents model uncertainty better than the standard (Bayesian) model does.

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.

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.

Imitating nature is an ever more popular strategy in many fields of science and engineering research, from ecological engineering to artificial intelligence. But while biomimetics and related fields have recently attracted increased attention from philosophers, there has been relatively little engagement with what I suggest we see as their basic epistemological presupposition: that we may acquire knowledge from nature. I argue that emphasizing and exploring this presupposition opens up a new approach to epistemology, based on a shift from a conventional epistemological relationship to nature as object of knowledge to a biomimetic relationship to nature as source of knowledge.

Oversight institutions that hold science and scientists accountable for responsible science have thus far not focused on the general societal impact of science. Responsibility for societal impact is now a pervasive aspect of scientific practice, but accountability remains elusive. I argue here that we should proceed cautiously, and that only clear and precise floors for responsibility should have accountability mechanisms. For the remainder of societal responsibilities in science, we should institutionalize assistive ethical mechanisms, which help scientists meet their responsibilities and share rather than offload ethical labor.

Recent developments in generalized probability theory have renewed a debate about whether regularity (i.e., the constraint that only logical contradictions get assigned probability $0$) should be a necessary feature of both chances and credences. Crucial to this debate, however, are some mathematical facts regarding the interplay between the existence of regular generalized probability measures and various cardinality assumptions. We improve on several known results in the literature regarding the existence of regular generalized probability measures. In particular, we give necessary and sufficient conditions for the existence of regular generalized probability measures defined on the whole powerset of any sample space.

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.

Behavioral welfare economics usually aims at mere means paternalism, helping agents better pursue their own goals. This paper discusses one initially promising way to inform policies addressed at agents who violate expected utility theory (EUT), namely what I call “CPT debiasing.” I argue that this approach is problematic even if we grant the normative authority of EUT, the descriptive adequacy of CPT (cumulative prospect theory), and the general acceptability of means paternalism. First, it is doubtful whether the CPT utility function measures what its proponents intend. Second, by imposing risk neutrality on agents, the approach involves a more problematic paternalism.