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The Great Regression Machine Learning, Econometrics, and the Future of Quantitative Social Sciences
IPOPS SAGE, France.
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. CNRS SAGE, France.
2018 (English)In: Revue française de sociologie, ISSN 0035-2969, E-ISSN 1958-5691, Vol. 59, no 3, p. 475-506Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
EDITIONS OPHRYS , 2018. Vol. 59, no 3, p. 475-506
Keywords [en]
SUPERVISED MACHINE LEARNING; ECONOMETRICS; QUANTITATIVE SOCIAL SCIENCES; KNOWLEDGE
National Category
Learning
Identifiers
URN: urn:nbn:se:liu:diva-153191DOI: 10.3917/rfs.593.0475ISI: 000450137500005OAI: oai:DiVA.org:liu-153191DiVA, id: diva2:1267267
Note

Funding Agencies|ERC [324233]; Riksbankens Jubileumsfond [DNR M12-0301:1]; Swedish Research Council [DNR 445-2013-7681, DNR 340-2013-5460]; Excellence Initiative of the University of Strasbourg

Available from: 2018-11-30 Created: 2018-11-30 Last updated: 2018-11-30

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • Other style
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Language
  • de-DE
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Output format
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