Learning Machines: Pedagogy, Academic-Industrial Collaboration, and Knowledge Work in the Russian Data Sciences

[摘要] This dissertation focuses on elite efforts to restructure work and education in the Moscow information technology sector. Russia has long had a strong national program in theoretical mathematics, but has been less successful at applying this expertise to the development of modern computational science, infrastructure, and business. As the Russian extractive economy stagnates, however, these elites are looking to data science as a privileged locus for the translation of what they call the ;;human resources” of excellence in fundamental mathematics into the ;;human capital” of data-scientific expertise. Their interventions into the science system have brought together industrial and academic actors in locally unprecedented ways, producing hybrid institutions, forms of pedagogy, and work practices that draw upon but differ strikingly from those operative in other knowledge economies. At the level of quotidian experience, this project traces the hybrid educational and work practices emerging within the new ecology of data scientific knowledge centered on a new department of computer science at the Higher School of Economics and Yandex. More broadly, it charts the ongoing institutional reformation of the Russian science system and information technology sector, following postsocialist knowledge workers as they develop sophisticated local forms of algorithmic rationality and pedagogy. In short, this research provides an intimate picture of work and education in the production of a distinctly Russian form of computational modernity.

[发布机构] Rice University

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