Because the machine can discriminate - How machine learning serves and transforms biological explanations of human difference

Aug 4, 2026

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Research on scientific/intellectual movements and social movements typically emphasizes external conditions such as funding, publication venues, and the prestige of movement actors rather than the movements’ internal substance. Using Pinch’s technologies-as-institutions framework, the study argues that research methods themselves can function as movement resources by institutionalizing ideas through research practice. The case of neuroscience shows that adopting machine learning reshaped measurement and modeling of group difference.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Core argument: technologies-as-institutions and methods as resources

Case study: neuroscience and sex differences movement

Implications for other human group categories

Original Research Article Because the machine can discriminate: How machine learning serves and transforms biological explanations of human difference Jeffrey W. Lockhart 1 Big Data & Society January–June: 1–14 © The Author(s) 2023 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20539517231155060](DOI: 10.1177/20539517231155060)[ ](DOI: 10.1177/20539517231155060)[journals.sagepub.com/home/bds](journals.sagepub.com/home/bds) Abstract Research on scientiﬁc/intellectual movements, and social movements generally, tends to focus on resources and conditions outside the substance of the movements, such as funding and publication opportunities or the prestige and networks of movement actors. Drawing on Pinch’s theory of technologies as institutions, I argue that research methods can also serve as resources for scientiﬁc movements by institutionalizing their ideas in research practice. I demonstrate the argument with the case of neuroscience, where the adoption of machine learning changed how scientists think about measurement and modeling of group difference. This provided an opportunity for members of the sex difference movement by offering a ‘truly categorical’ quantitative methodology that aligned more closely with their understanding of male and female brains and bodies as categorically distinct. The result was a ﬂurry of publications and symbiotic relationships with other researchers that rescued a scientiﬁc movement which had been growing increasingly untenable under the prior methodological regime of univariate, frequentist analyses. I call for increased sociological attention to the inner workings of technologies that we typically black box in light of their potential consequences for the social world. I also suggest that machine learning in particular might have wide-reaching implications for how we conceive of human groups beyond sex, including race, sexuality, criminality, and political position, where scientists are just beginning to adopt its methods. Keywords Gender, sex, machine learning, neuroscience, scientiﬁc/intellectual movements, research methods Scientists have long engaged in a set of related debates about whether human social groups of race, class, gender, and more constitute biologically distinct categories, and these debates remain lively today (Lockhart, 2021; Morning, 2014; Sanz, 2017) . I argue that part of the way they have remained lively in recent decades is through the adoption, on all sides, of machine learning (ML) . In this paper, I focus on how the introduction of ML to neuroscience rescued the foundering scientiﬁc/intellectual movement (SIM) for sex differences, even elevating its object to a gold standard, by preserving core theories and agendas while radically altering the ﬁeld’s quantitative reasoning. In particular, drawing on theories of technologies as institutions (Pinch, 2008), I argue that ML serves to institutionalize a frame of understanding for group difference that is favorable to the sex difference movement as part of general research practice in neuroscience. This means that ML and research methods in general can serve as resources for SIMs, extending existing theorizations that focus on traditional factors such as funding, publication, and prestige (Frickel and Gross, 2005) to show that the content and practice of research itself can be key resources for movements. Understanding this process requires not only a close analysis of how the use and results of a method are framed by SIM members but also an analysis of the implementation and workings ofthe method itself as a technology, to understand how SIM norms and values are delegated to and materialized in it (Bucher, 2016) . While the values and frames embedded in ML have been extensively studied (e.g. Benjamin, 2019; Chun, 2021; Eubanks, 2017; Fourcade and Healy, 2016; Keyes, 2018; Noble, 2018; Scheuerman et

FAQs

What does the article argue about research methods and scientific/intellectual movements?

It argues that research methods can act as resources for movements by institutionalizing their ideas in research practice, not only by external support such as funding or prestige.

How does machine learning change neuroscience research on group difference in the article’s case study?

The adoption of machine learning alters how scientists think about measurement and modeling of group difference, and it supports a “truly categorical” quantitative approach aligned with sex difference views.

Why does the article emphasize discovery algorithms beyond production algorithms?

It notes that prior work focuses heavily on production algorithms used by governments and companies, while discovery algorithms used in scientific learning and claim-making have received less attention regarding embedded values and frames.

Aug 4, 2026

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