This paper recapitulates earlier work in which I argued for the disunity of science, the plurality of partly incommensurable ways in which the world can be conceptualised for scientific purposes. It then aims to show how this plurality is intelligible, even to be expected, from the perspective of a process philosophy that sees the world as largely disorganised, but as allowing the emergence of pockets of stability, most notably the stability provided by biological organisms. It incidentally aims to demonstrate the importance to one another of science and metaphysics.

The interaction between scientific fields may be subject to strategic constraints—that is, fruitful interaction between fields may require adapting one field to the strategies informing the other. One way the concepts within an interfield field develop is by recrafting concepts originating in one practice in terms compatible with the strategies guiding the other. This conceptual accommodation may be a prerequisite to fruitful interactions between fields with different central problems. I explore these themes by considering the strategic constraints operative in the interfield field of quantum chemistry and the conceptual accommodation required for application to organic chemistry.

This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.

In noncausal explanations, some noncausal facts (such as mathematical, modal, or metaphysical) are used to explain some physical facts. However, precisely because these explanations abstract away from causal facts, they face two challenges: (1) it is not clear why one rather than the other noncausal explanantia would be relevant for the explanandum; and (2) why would standing in a particular explanatory relation (e.g., “counterfactual dependence,” “constraint,” “entailment,” “constitution,” and “grounding”), and not in some other, be explanatory. I develop an explanatory relevance account that is based on erotetic constraints and show how it addresses these two challenges.

Benchmark tests are employed when testing for racial discrimination by police. Neil and Winship (2019) emphasize that such tests are threatened by Simpson’s paradox, but they avoid analyzing the paradox causally. They consequently cannot elucidate the link between statistical quantities and discrimination hypotheses. Simpson’s paradox reveals that the statistics given by benchmark tests are not invariant to conditioning on additional variables. On this basis, I argue that benchmark statistics should not by themselves be taken to provide any evidence regarding discrimination, absent additional assumptions. Causal models can represent these assumptions.

This paper proposes a novel view in the the philosophy of race & causation literature known as “causal agnosticism” about race. Causal agnosticism about race implies that it is reasonable to refrain from making judgments about whether race is a cause. The paper’s thesis asserts that certain conditions must be met to infer that something is a cause, according to the fundamental assumptions of causal inference. However, in the case of race, these conditions are often violated. By advocating for causal agnosticism, the paper suggests a more modest approach to understanding the role of race in causal relationships.

It is increasingly easy to acquire a large amount of data about a problem before formulating a hypothesis. The idea of exploratory data analysis (EDA) predates this situation, but many researchers find themselves appealing to EDA as an explanation of what they are doing with these new resources. Yet there has been relatively little explicit work on what EDA is or why it might be important. I canvass several positions in the literature, find them wanting, and suggest an alternative: exploratory data analysis, when done well, shows the expected value of experimentation for a particular hypothesis.

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 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.

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.

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.

This paper articulates a conceptual framework for examining philosophical issues such as the role of values in science at an organizational level. It distinguishes between three dimensions of organizations – organizational aims, organizational structure, and organizational culture – and it examines how these dimensions relate to values in research and development, with a focus on machine learning systems for predictive policing. This framework can be fruitful in identifying interesting and understudied philosophical problems – including those involving inter-organizational divisions of labor – that might otherwise be difficult to conceptualize.

Consensus reporting is valuable for presenting unified scientific evidence to the public. When a consensus does not exist, I argue that scientists ought not to default to majority reporting in its place. Majority reporting has several epistemic drawbacks because it can obscure underlying justifications and lines of evidence, which may be in conflict or contested. I argue that minority reporting, in conjunction with majority reporting, is an epistemically superior mechanism for scientists to report on the full range of reasons and evidence available within a group. This article addresses several objections, including worries over group cohesion, fringe reporting, and elite capture.

We consider the long-term evolution of science and show how a “contagion of disrespect”—an increasing dismissal of research in subfields associated with marginalized groups—can arise due to the dynamics of collaboration and reputation (versus, e.g., preconceived notions of the field’s worth). This has implications both for how we understand the history of science and for how we attempt to promote diverse scientific inquiry.

When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology.

Microaggressions are hypothesized to play a causal role in undesirable population effects such as racial health gaps, but the mechanisms through which this occurs are not yet well understood. I call inquiry about these mechanisms the “explanatory project.” I suggest that the explanatory project has been hindered by microaggression concepts tailored to be applicable under conditions of lived uncertainty, rather than to facilitate understanding of structural causes. I defend a pluralist, structural account of microaggressions from objections by Regina Rini that, while appropriate for ethical projects, do not apply to the explanatory project.

This article addresses problems with a defensive turn in discussions of science and Indigenous ways of knowing, being, and doing. Philosophers and practitioners of science have focused recent discussions on coarse-grained questions of demarcation, epistemic parity, and identity—asking questions such as “Is Indigenous knowledge science?” Using representative examples from Aotearoa New Zealand, we expose rampant ambiguities in these arguments, and show that this combative framing can overlook what is at stake. We provide a framework for analyzing these problems and suggest better ways forward.

Transdisciplinary research knits together knowledge from diverse epistemic communities in addressing social-environmental challenges, such as biodiversity loss, climate crises, food insecurity, and public health. This article reflects on the roles of philosophy of science in transdisciplinary research while focusing on Indigenous and other subjugated forms of knowledge. We offer a critical assessment of demarcationist approaches in philosophy of science and outline a constructive alternative of transdisciplinary philosophy of science. While a focus on demarcation obscures the complex relations between epistemic communities, transdisciplinary philosophy of science provides resources for meeting epistemic and political challenges of collaborative knowledge production.

We consider some of the epistemic benefits of exploring “theory space” in the context of modifications of general relativity with intended applications in cosmology. We show how studying modifications of general relativity can help in assessing the robustness of empirical inferences, particularly in inaccessible regimes. We also discuss challenges to sharply distinguishing apparently distinct directions in theory space.