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The Rise of Machine Learning in the Academic Social Sciences

Author

Listed:
  • Rahal, Charles
  • Verhagen, Mark D.
  • Kirk, David

    (University of Oxford)

Abstract

This short data visualisation and accompanying perspective explains recent trends and outlines three reasons to be even more optimistic about the future of Machine Learning in the academic Social Sciences.

Suggested Citation

  • Rahal, Charles & Verhagen, Mark D. & Kirk, David, 2021. "The Rise of Machine Learning in the Academic Social Sciences," SocArXiv gydve_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:gydve_v1
    DOI: 10.31219/osf.io/gydve_v1
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    References listed on IDEAS

    as
    1. Cummins, Neil, 2021. "Where Is the Middle Class? Evidence from 60 Million English Death and Probate Records, 1892–1992," The Journal of Economic History, Cambridge University Press, vol. 81(2), pages 359-404, June.
    2. Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021. "Integrating explanation and prediction in computational social science," Nature, Nature, vol. 595(7866), pages 181-188, July.
    Full references (including those not matched with items on IDEAS)

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