Marina DiMarco

In 2016, the US National Institutes of Health implemented the Sex As a Biological Variable policy (SABV), mandating that researchers collect and report data about sex in preclinical biomedical research. Janet Rich-Edwards and Donna Maney ("Best practices to promote rigor and reproducibility in the era of sex-inclusive research." Elife 12 (2023): e90623) have recently drawn attention to an important distinction among research projects under this policy: while some seek to test hypotheses about sex differences, others are properly understood as exploratory. Here, I consider the consequences of this distinction for evaluating the pursuitworthiness of experiments under SABV. Because exploration under SABV piggybacks on confirmatory experimentation and constitutes a cross-contextual search for effects of sex, we should evaluate its pursuitworthiness under Pierre-Hugues Beauchemin and Kent Staley’s account of exploratory inquiry, which does not require exploration to take place in its own type of experiment ("The epistemological significance of exploratory experimentation: A pragmatist model of how practices matter philosophically." European Journal for Philosophy of Science 14, no. 4 (2024): 1–33). One upshot of this evaluation is that if exploratory surveillance under SABV involves openness to unanticipated findings, as exploration often does, it may be strategic for exploratory inquirers to retain loose, multidimensional conceptualizations of sex. I analyze how this might inform recent calls for scientists to make their operationalizations of sex theoretically-informed and precise. Precision may not be the best or the only way forward for sex in biomedicine.

Machine learning offers new possibilities for developing more precise diagnostics and treatments, but the increasing use of sex stratification in precision medicine algorithms raises concerns. Using Alzheimer's disease (AD) research as an example in which machine learning approaches are applied to a heterogenous, socially patterned disease, this paper examines how the move toward sex-specific “pink” and “blue” algorithms reinforces biological sex essentialist assumptions and their attendant harms. We analyze three examples of sex-stratified algorithmic approaches in AD research, and identify three interacting processes-effacing contested knowledge, obscuring social factors, and ossifying binary sex categories-that can occur when binary sex variables are incorporated into predictive models. These case studies demonstrate that even in models intended to be causally agnostic, sex categories are likely to be interpreted as decontextualized, self-evident health determinants in a manner that can imply causality of biological sex. We call for establishing ethical norms and empirical standards for including gender/sex variables in precision medicine algorithms to avoid perpetuating crude ontologies of sex and gender that undermine both scientific validity and health justice.

The concept of biological sex guides research, clinical practice, science funding policy, and contemporary political discourse. Despite some substantive differences, all existing candidate philosophical accounts of sex assume its legitimacy as a biological concept. Here, we challenge this view. We argue against realism about biological sex, and that eliminating biological sex from large swaths of biological theory and practice may be preferable compared to conventionalist or fictionalist anti-realisms. There are serious social and epistemic costs to using “biological sex” in place of more specific alternatives. Because of this, biologists and philosophers of science should consider eliminativism about the concept of biological sex. Biological sex eliminativism is worth taking seriously, and it can play important roles in philosophical debate and biological practice, even for those who remain skeptical. The methodological consequences of biological sex eliminativism are compatible with best practices for inquiry in the biological and biomedical sciences, with inclusive approaches to the study of sex and gender, and with feminist philosophical and methodological recommendations. Taking eliminativism seriously reveals important disagreement about the work that a concept of biological sex should do, and imposes a contrastive burden on would-be rivals.

Extant accounts of trust in science focus on reconciling scientific and public value judgments, but neglect the challenge of learning audience values. I argue that for scientific experts to be epistemically trustworthy, they should adopt a cooperative approach to learning about the values of their audience. A cooperative approach, in which expert and non-expert inquirers iteratively refine value judgments, better achieves important second-order epistemic dimensions of trustworthiness. I suggest that strong objectivity in the feminist standpoint theoretic sense is sometimes a prerequisite for trustworthiness.

Criticism is a staple of the scientific enterprise and of the social epistemology of science. Philosophical discussions of criticism have traditionally focused on its roles in relation to objectivity, confirmation, and theory choice. However, attention to criticism and to criticizability should also inform our thinking about scientific pursuits: the allocation of resources with the aim of developing scientific tools and ideas. In this paper, we offer an account of scientific pursuitworthiness which takes criticizability as its starting point. We call this the apokritic model of pursuit. Its core ideas are that pursuits are practices governed by norms for asking and answering questions, and that criticism arises from the breach of these norms. We illustrate and advertise our approach using examples from institutional grant review, neuroscience, and sociology. We show that the apokritic model can unify several indices of criticizability, that it can account for the importance of criticizing pursuits in scientific practice, and that it can offer ameliorative advice to erstwhile pursuers.

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts.

In their 1987 Nature publication, “Mitochondrial DNA and Human Evolution,” Rebecca Cann, Mark Stoneking, and Allan C. Wilson gave a new reconstruction of human evolution on the basis of differences in mitochondrial DNA among contemporary human populations. This phylogeny included an African common ancestor for all human mitochondrial DNA (mtDNA) lineages, and Cann et al.’s reconstruction became known as the “Out of Africa” hypothesis. Since mtDNA is inherited exclusively through the maternal line, the common ancestor who was first branded African Eve later became known as Mitochondrial Eve (mtEve, for short). In this paper, I show that mtEve was not a single, successful, or purely scientific discovery. Instead, she was produced many times and in many ways, each of which informed the next. Importantly, though Wilson and colleagues heralded mitochondrial DNA as a source of certainty, objectivity, and consensus for evolutionary inference, their productions of Mitochondrial Eve depended as much on popular assumptions about the certainty of maternal inheritance as they did on new molecular and computational tools. This recognition lets us reevaluate the complex consequences of these productions, which, like mtEve herself, could not be confined to a purely social, material, or scientific dimension.