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2 books match the query 'Dixon-Román, Ezekiel J.'
Inheriting Possibility
Social Reproduction and Quantification in EducationEzekiel J. Dixon-Román
University of Minnesota Press, 2017
How has the dominant social scientific paradigm limited our understanding of the impact of inherited economic resources, social privilege, and sociocultural practices on multigenerational inequality? In what ways might multiple forces of social difference haunt quantitative measurements of ability such as the SAT? Building on new materialist philosophy, Inheriting Possibility rethinks methods of quantification and theories of social reproduction in education, demonstrating that test performance results and parenting practices convey the impact of materially and historically contingent patterns of differential possibility.
Ezekiel J. Dixon-Román explores the dualism of nature and culture that has undergirded theories of inheritance, social reproduction, and human learning and development. Research and debate on the reproduction of power relations have rested on a premise that nature is made up of fixed universals on which the creative, intellective, and discursive play of culture are based. Drawing on recent work in the physical and biological sciences, Dixon-Román argues that nature is culture. He contends that by assuming a rigid nature/culture binary, we ultimately limit our understanding of how power relations are reproduced.
Through innovative analyses of empirical data and cultural artifacts, Dixon-Román boldly reconsiders how we conceptualize the processes of inheritance and approach social inquiry in order to profoundly sharpen understanding and address the reproducing forces of inequality.
Learning Under Algorithmic Conditions
Elizabeth de Freitas
University of Minnesota Press, 2026
Exploring the influence of AI technologies on theories of reason, cognition, learning, and education
Learning Under Algorithmic Conditions presents twenty-seven concise essays that collectively chart the shifting terrain of learning in the age of artificial intelligence. Providing historical and philosophical context, this innovative volume features prominent scholars from the fields of media studies, philosophy, and education research, who shed light on how learning has become newly envisioned, machinic, and more-than-human. The contributors unravel various histories of machine intelligence and elucidate the current impact of machine learning technologies on practices of knowledge production. Teeming with theoretical and practical insights, Learning Under Algorithmic Conditions is an interdisciplinary guide for those working across the humanities and social sciences as well as anyone interested in understanding our changing social, political, and technical infrastructures.
Contributors: Craig Carson, Adelphi U; Felicity Coleman, U of the Arts London; Ed Dieterle; Shayan Doroudi, U of California, Irvine; David Gauthier, Utrecht U; Cathrine Hasse, Aarhus U; Talha Can İşsevenler, CUNY; Goda Klumbytė; Robb Lindgren, U of Illinois Urbana-Champaign; Michael Madiao; Henry Neim Osman; Luciana Parisi, Duke U; Carolyn Pedwell, Lancaster U; Arkady Plotnitsky, Purdue U; Julian Quiros, U of Pennsylvania; Sina Rismanchian; Warren Sack, U of California, Santa Cruz; R. Joshua Scannell, The New School; Gregory J. Seigworth, Millersville U; Rebecca Uliasz, U of Michigan; David Wagner, U of New Brunswick; Ben Williamson, U of Edinburgh.
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