In general relativity, the strong equivalence principle is underpinned by a geometrical account of fields on spacetime, by which all fields and bodies probe the same geometry. This geometric account implies that the parallel transport of all spacetime tensors and spinors is dictated by a single affine connection. No similar account of gauge theory is put forward by standard textbooks, which use principal bundles to coordinate the parallel transport of different, interacting particles. Nonetheless, here I argue that gauge theory does afford such a geometric account, obviating the need for principal bundles.

It is often said that successful scientific research must be built on trust. Focusing on the alleged necessity of trust for successful scientific communication and thus for scientific cooperation (which underlies much of contemporary science), I argue that science mustn’t be built on trust. Appearances to the contrary come from a failure to distinguish different attitudes toward scientists’ testimony, in particular, trusting and relying on other scientists. This article proposes an account of scientific reliance and explains how it differs from scientific trust; it also shows why this distinction matters for science.

Scientists have the epistemic responsibility of producing knowledge. They also have the social responsibility of aligning their research with the needs and values of various societal stakeholders. Individual scientists may be left with no guidance on how to prioritize and carry these different responsibilities. As I will argue, however, the responsibilities of science can be harmonized at the collective level. Drawing from debates in moral philosophy, I will propose a theory of the collective responsibilities of science that accounts for the internal diversity of research groups and for their different responsibilities.

Tarja Knuuttila and Mary Morgan recently challenged the widespread understanding that deidealization is no more than a simple process of relaxing assumptions to build increasingly more realistic models. They submit that, in practice, processes of model deidealization are diverse and complex and thus warrant more explicit scrutiny. Drawing on a case from economics, I extend their proposal by showing how narratives, as additional representational forms, can assume a crucial role in deidealizing mathematical models. I thereby propose to consider that processes of model deidealization are not necessarily exhausted by processes in which one mathematical model is replaced with another one.

It is sometimes assumed that large language models (LLMs) know language, or for example that they know that Paris is the capital of France. But what—if anything—do LLMs actually know? In this paper, I argue that LLMs can acquire tacit knowledge as defined by Martin Davies (1990). Whereas Davies himself denies that neural networks can acquire tacit knowledge, I demonstrate that certain architectural features of LLMs satisfy the constraints of semantic description, syntactic structure, and causal systematicity. Thus, tacit knowledge may serve as a conceptual framework for describing, explaining, and intervening on LLMs and their behavior.

Does low mood have intentional content? If so, what is it? Philosophers have tried to answer both questions by appealing to low mood’s phenomenal character. However, appeals to phenomenology have not settled this debate. Thus, I take a different approach: I tackle both questions by examining low mood’s complex functional role in cognition. I argue that if we take this role into account, we have excellent reason to believe that low mood (a) has content, and (b) has the following indicative-imperative content: Good events are, on average, less likely to occur than bad events and limit [the subject’s] resource expenditure!

This article presents a novel sense in which theoretical structure has been preserved across the transition from classical to quantum physics. I import mathematical tools from category theory that have been used for structural comparisons in the context of theoretical equivalence and apply these tools to new situations involving theory change. The structural preservation takes the form of a categorical equivalence between categories of models of classical and quantum physics. I situate the significance of this structural preservation in terms of prospects for theory construction in quantum physics.

In this article, I stress the need to broaden the scope of diversity in value-laden ideals of science to include geographic diversity. I argue that egalitarian and normic ideals have conceptual limitations when considering this dimension. While egalitarian frameworks advocate for a placeless science, normic frameworks predominantly locate scientific knowledge within the “Global North,” highlighting the importance of including “non-Western” perspectives from the “Global South.” These limitations have negative and unjust epistemic consequences: They risk perpetuating cultural imperialism, reproducing a colonial epistemic norming of space, and committing epistemic exoticization toward scientific communities in subaltern regions.

We examine whether the “privileged coordinates” of a geometric space encode its “amount of structure.” In doing so, we compare this coordinate approach to comparing amounts of structure to the more familiar automorphism approach. We first show that on a natural understanding of the former, it faces one of the same well-known problems as the latter. We then capture a precise sense in which the two approaches are closely related to one another, and we conclude by discussing whether they might still prove useful in cases of philosophical interest, despite their shortcomings.

One type of computational indeterminacy arises from partitioning a system’s physical state space into state types that correspond to the abstract state types underlying the computation concerned. The mechanistic individuative strategy posits that computation can be uniquely identified through either narrow physical properties exclusively or wide, proximal properties. The semantic strategy posits that computation should be uniquely identified through semantic properties. We develop, and defend, an alternative functional individuative strategy that appeals—when needed—to wide, distal functions. We claim that there is no actual computation outside of a functional context. Desiderata for the underlying notion of teleofunction are discussed.

There is a growing consensus among philosophers that quantifying value-laden concepts can be epistemically successful and politically legitimate if all value-laden choices in the process of quantification are aligned with stakeholder values. I argue that proponents of this alignment approach have failed to argue for its basic premise: Successful quantification is sufficiently unconstrained to be achievable along multiple, stakeholder-specific pathways. I then challenge this premise by considering a rare example of successful value-laden quantification in seismology, in which stakeholder values had to be disregarded from measure design and testing. The example motivates my contention that value alignment is not a workable source of political legitimacy for successful programs of quantification.

Traditional wisdom dictates that statistical model outputs are estimates, not measurements. Despite this, statistical models are employed as measurement instruments in the social sciences. In this article, I scrutinize the use of a specific model—the logit model—for psychological measurement. Given the adoption of a criterion for measurement that I call comparability, I show that the logit model fails to yield measurements due to properties that follow from its fixed residual variance.

We evaluate the roles general relativistic assumptions play in simulations used in recent observations of black holes including with LIGO-Virgo and the Event Horizon Telescope. In both experiments, simulations play an ampliative role, enabling the extraction of more information from the data than would be possible otherwise. This comes at a cost of theory-ladenness. We discuss the issue of inferential circularity, which arises in some applications; classify some of the epistemic strategies used to reduce the extent of theory-ladenness; and discuss ways in which these strategies are model independent.

How do opinion diversity and belief polarisation affect epistemic group decision-making, particularly if decisions must be made without delay and on the basis of permissive evidence? In an agent-based model, we track the consistency of group opinions aggregated through sentence-wise majority voting. Simulations on the model reveal that high opinion diversity, but not polarisation, incurs a significant inconsistency risk. These results indicate that epistemic group decisions based on permissive evidence can be particularly difficult for diverse groups. The results also improve our understanding of what can reasonably be expected of expert groups, and where expert advice might have limits.

In this paper I offer a model-theoretic interpretation of Autonomy Theory as defended by Moreno, Mossio, Montévil, and Bich. I address accusations that Autonomy Theory is excessively liberal, such as those made by Garson (2017), arguing that these misunderstand the role of strategic abstractions and generalizations in theory construction. Conceiving of closure of constraints as a model-building effort that emphasizes generality—in the spirit of Levins (1966)—also clarifies its potential for application in empirical contexts.

The perception/action model posits distinct streams of visual processing for perception and online motor guidance. This model is apparently supported by experiments showing that visual illusions affect action tasks less than perception. In recent years, however, critics have argued against both the validity of these experiments and their support (irrespective of their validity) for the perception/action model. In this article, I reexamine this psychophysical evidence. I argue that it strongly supports the existence of distinct representations for “perception” and “action” but only moderately supports the existence of distinct systems generating those representations.

Douglas has argued that if values were accorded a “direct” role during the “internal” phases of science, this would amount to “wishful thinking” in place of evidence. This article draws on two claims that jointly threaten this position. First, building a cogent ethical case in favor of a claim about what ought to happen is not a simple matter of saying what one wishes were true; a good ethical case has a kind of discipline to it. Second, some theorists have argued that scientists do and should defend “mixed hypotheses,” that is, hypotheses that implicate both factual and evaluative content.

Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of “terrorist”’ in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches.

This essay introduces, develops, and appraises the mitonuclear compatibility species concept (MCSC), identifying advantages and limitations with respect to alternative species concepts. While the consensus amongst most philosophers of biology is that (kind) essentialism about species is mistaken, and that species at most have relational essences, we appeal to the MCSC to defend thoroughgoing intrinsic essentialism. Namely, the doctrine that species have fully intrinsic essences and, thus, are natural kinds (of sorts), while allowing that species aren’t categorically distinct.

Paleontology is facing an ethical crisis related to its long history of extractive practices, including a practice now referred to as parachute science. In this article, I provide diagnostic criteria for identifying parachute science and illustrate them using the high-profile example of a Brazilian dinosaur fossil, Irritator challengeri, acquired by a German museum under dubious conditions. I use this case study to identify three types of harm resulting from parachute science, showing how they can be understood as a case of distributive epistemic injustice. I conclude by using this framework to point toward more ethical paleontological practices.