Jha et al. (2024) offer several objections to Lange’s account of “distinctively mathematical” scientific explanations (DMEs). This article argues that these objections fail. Jha et al.’s arguments fail to show that Lange is inconsistent in characterizing DMEs—that, by Lange’s lights, every causal explanation involving mathematical facts is a DME. Jha et al.’s arguments fail to suggest that on Lange’s account, DME’s are too common or explanatorily insubstantial to underwrite any philosophical lessons about explanation. Jha et al.’s arguments also fail to show that Lange relies on underhanded manipulations of the explananda targeted by DMEs.

This article proves two no-go results against the conventionality of geometry. I then argue that any remaining conventionality arises from scientific incompleteness. I illustrate by introducing a new kind of conventionality arising in the presence of higher spatial dimensions, where the incompleteness is resolved by introducing new physical theories like Kaluza–Klein theory. Thus, conventional choices of this kind may guide scientific discovery, but if successful, they would dissolve the original conventional freedom.

Numerous phenomena prompt inquiries into their origins, spanning from the cosmos and life, to species, civilizations, and pandemics. Answering these questions entails offering origin explanations. Here, I explore the distinctive characteristics of origin explanations that distinguish them from other types of explanations. I explicate the concept of an origin phenomenon and suggest conceptualizing an origin explanation as a specific form of causal explanation—one that reveals a bottleneck in the causal network leading to an origin phenomenon. The resulting framework highlights the specificity of origin explanations across disciplines, pinpointing distinctive topological features within the causal structure of the world.

Philosophers have spilled much ink over the discovery of ideas in the classical “context of discovery.” However, there has been little engagement with the question of what constitutes a discovery of “things in the world.” A much-overlooked answer to this question is provided by T. S. Kuhn. In this article, I show that discoveries awarded the Nobel Prize in Physics over the past 53 years accord with a basic premise of Kuhn’s account and his distinction between two types of natural kind discoveries. I also draw normative conclusions for credit attribution in science.

According to what I call the Probabilistic View, absence of evidence is evidence of absence when finding evidence is highly expected. However, this view fails to make sense of the practice of using absence of evidence in the paleosciences, where finding evidence is typically not highly expected. Using a case from paleogeology, I offer a novel account of when absence of evidence should be evidence of absence, which I call the Pragmatic View: Appeals to absence of evidence as evidence of absence are warranted because they offer a scaffold to investigate auxiliary hypotheses related to the hypothesis in question.

This paper focuses on a type of underdetermination that has barely received any philosophical attention: underdetermination of data. I show how one particular type of data—RNA sequencing data, arguably one of the most important data types in contemporary biology and medicine—is underdetermined, because RNA sequencing experiments often do not determine a unique data set. Instead, different ways of generating usable data can result in vastly different, and even incompatible, data sets. But, since it is often impossible to adjudicate among these different ways of generating data, ‘the data’ coming out of such experiments is underdetermined.

Cognitive neuroscientists typically posit representations that relate to various aspects of the world, which philosophers call representational content. Anti-realists about representational content argue that contents play no role in neuroscientific explanations of cognitive capacities. In this paper, I defend realism against an anti-realist argument due to Frances Egan, who argues that for content to be explanatory it must be both essential and naturalistic. I introduce a case study from cognitive neuroscience in which content is both essential and naturalistic, meeting Egan’s challenge. I then spell out some general principles for identifying studies in which content plays an explanatory role.

Current models of scientific inquiry assume that scientists all share the same evaluative standards. However, scientists often rely on different yet legitimate ones, a feature we call evaluative diversity. We investigate how scientific success is affected by diversity in evaluative standards through computer-based simulations. Our results show that communities with diverse standards benefit substantially from scientists sharing all the approaches they explored, regardless of whether they considered them valuable. Moreover, we find that even a moderate degree of evaluative diversity can, under certain conditions, lead scientists to reach more satisfying results than those they would reach in homogeneous communities.

The sex binary can be a useful idealization in scientific models; a fiction helpful for sorting people into two categories to investigate the possible mechanisms driving the differences we observe. However, attempts to say that the sex binary is anything more than that are misguided. Alisa Bokulich’s account of credentialed fictions will help substantiate this point to articulate a more nuanced view of the fictional sex binary in modeling terms. I will argue that, despite often treating it as such, the sex binary is not an approximation of biological sex nor is it explanatory.

A recent dispute in political science raises issues about the objectivity of measures of democracy. Political scientists Little and Meng argue that democracy indices using country experts as coders show a greater degree of democratic backsliding than do measures that are objective. They worry that this discrepancy may reflect coder bias. I distinguish three aspects of objectivity and offer a reconceptualization of objectivity as coherence objectivity. I argue that coherence objectivity is better suited for evaluating measures of social science concepts like democracy than the understanding of objectivity implicit in Little and Meng’s discussion.

Philosophers have been drawing their attention to the metaphysics of symmetries. Most have taken a Closed System View, that is, the metaphysical assumption that closed systems are ontologically fundamental. In this article, I explore the consequences of adopting a different perspective, the Open System View, according to which open systems are regarded as ontologically fundamental. I argue that by doing so, our metaphysical understanding of symmetries changes substantially in three respects: Epistemic approaches are favored, ontic approaches should change the set of physical symmetries regarded as fundamental, and the metaphysical connection between conservation principles and symmetries is weaker than thought.

What is lost if we don’t have a diagnosis? This article examines the aims of clinical medicine and the role of understanding in these aims. Starting from a case prompt with a patient suffering from persistent physical symptoms, I argue that understanding is at the clinical core and that the target of such understanding is the patient’s body with symptoms. Synthesizing accounts of medical understanding and phenomenology of illness, I suggest that the understanding sought in the clinic extends beyond mechanistic explanation to include a sense of bodily intelligibility and that diagnoses are useful but not necessary tools to this end.

Evidence synthesis has recently seen an enormous increase across many different scientific disciplines. Despite its popularity, it has also been the subject of significant criticism. One of the main critiques of evidence synthesis is the existence and treatment of heterogeneity between primary studies. The aim of this article is to reexamine heterogeneity in evidence synthesis, including perspectives from evolutionary biology, ecology, and conservation. I argue that while some of the critiques of heterogeneity remain valid, there are contexts where it is much less problematic. Furthermore, I propose that heterogeneity can be useful because analyzing it can provide valuable information.

This paper presents a new problem for the inference rule commonly known as “inference to the best explanation” (IBE). The problem is that uncertainty about parts of one’s evidence may undermine the inferrability of a hypothesis that would provide the best explanation of that evidence, especially in cases where there is an alternative hypothesis that would provide a better explanation of only the more certain pieces of evidence. A potential solution to the problem is sketched, in which IBE is generalized to handle uncertain evidence by invoking a notion of evidential robustness.

Eric Mandelbaum has reported some troubles with Bayesianism in cognitive science. He has brought some behavioral data to show that belief updating in humans is fundamentally Bayesian perverse. I argue that the behaviors that he seeks to explain do not undermine Bayesian accounts of belief updating and can instead be explained as idiosyncratic consequences of an appropriately bounded implementation of a Bayesian-normative belief-updating system.

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.