{"title":"Because the machine can discriminate: How machine learning serves and transforms biological explanations of human difference.","authors":"Jeffrey W Lockhart","doi":"10.1177/20539517231155060","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10704893/pdf/","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data & Society","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/20539517231155060","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/2/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍:
Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government.
BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices.
BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.