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Don´t Stop Me Now: Gender Attitudes in Academic Seminars Through Machine Learning

Author

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  • Mateo Seré

Abstract

This study, utilizing a novel dataset from economic seminar audio recordings, investigates gender-based peer interactions, structured around five key findings: (i) Female speakers are interrupted more frequently, earlier, and differently than males; (ii) the extra interruptions largely stem from female, not male, audience members; (iii) male participants pose fewer questions but more comments to female presenters; (iv) audience members of both genders interrupt female speakers with a more negative tone; (v) less senior female presenters receive more interruptions from women. Control variables include seminar series, presentation topic, and factors like presenter affiliation, seniority, and department ranking.

Suggested Citation

  • Mateo Seré, 2023. "Don´t Stop Me Now: Gender Attitudes in Academic Seminars Through Machine Learning," Working Papers 2309, Herman Deleeck Centre for Social Policy, University of Antwerp.
    • Handle: RePEc:hdl:wpaper:2309
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    File URL: https://medialibrary.uantwerpen.be/files/57001/652587d0-210f-43fd-8843-4e6252bef019.pdf
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    References listed on IDEAS

    as
    1. Claudia Goldin, 2014. "A Grand Gender Convergence: Its Last Chapter," American Economic Review, American Economic Association, vol. 104(4), pages 1091-1119, April.
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    Cited by:

    1. Pascaline Dupas & Amy Handlan & Alicia Sasser Modestino & Muriel Niederle & Mateo Seré & Haoyu Sheng & Justin Wolfers & Seminar Dynamics Collective, 2026. "Gender Differences in Economics Seminars," American Economic Review, American Economic Association, vol. 116(2), pages 749-789, February.

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