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  • 1.
    Boelaert, Julien
    et al.
    IPOPS SAGE, France.
    Ollion, Etienne
    Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. CNRS SAGE, France.
    The Great Regression Machine Learning, Econometrics, and the Future of Quantitative Social Sciences2018In: Revue française de sociologie, ISSN 0035-2969, E-ISSN 1958-5691, Vol. 59, no 3, p. 475-506Article in journal (Refereed)
    Abstract [en]

    What can social sciences do with machine learning, and what can the latter do to them? A contribution to the emerging debate on the role of machine learning for the social sciences, this article offers an introduction to this class of statistical techniques. It details its premises, logic, and the challenges it faces. This is done by comparing machine learning to more classical approaches to quantification - most notably parametric regression - both at a general level and in practice. The article is thus an intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science. We claim that the revolution announced by many and feared by others will not happen any time soon, at least not in the terms that both proponents and critics of the technique have spelled out. The growing use of machine learning is not so much ushering in a radically new quantitative era as it is fostering an increased competition between the newly termed classic method and the learning approach. This, in turn, results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall.

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