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Gender stereotypes are reflected in the distributional structure of 25 languages

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  • Molly Lewis

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Gary Lupyan

    (University of Wisconsin-Madison)

Abstract

Cultural stereotypes such as the idea that men are more suited for paid work and women are more suited for taking care of the home and family, may contribute to gender imbalances in science, technology, engineering and mathematics (STEM) fields, among other undesirable gender disparities. Might these stereotypes be learned from language? Here we examine whether gender stereotypes are reflected in the large-scale distributional structure of natural language semantics. We measure gender associations embedded in the statistics of 25 languages and relate these to data on an international dataset of psychological gender associations (N = 656,636). People’s implicit gender associations are strongly predicted by gender associations encoded in the statistics of the language they speak. These associations are further related to the extent that languages mark gender in occupation terms (for example, ‘waiter’/‘waitress’). Our pattern of findings is consistent with the possibility that linguistic associations shape people’s implicit judgements.

Suggested Citation

  • Molly Lewis & Gary Lupyan, 2020. "Gender stereotypes are reflected in the distributional structure of 25 languages," Nature Human Behaviour, Nature, vol. 4(10), pages 1021-1028, October.
    • Handle: RePEc:nat:nathum:v:4:y:2020:i:10:d:10.1038_s41562-020-0918-6
    DOI: 10.1038/s41562-020-0918-6
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    Cited by:

    1. van Loon, Austin, 2022. "Three Families of Automated Text Analysis," SocArXiv htnej, Center for Open Science.
    2. Alina Arseniev-Koehler & Jacob G. Foster, 2022. "Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat," Sociological Methods & Research, , vol. 51(4), pages 1484-1539, November.
    3. Jessica Brough & Lasana T. Harris & Shi Hui Wu & Holly P. Branigan & Hugh Rabagliati, 2024. "Cognitive causes of ‘like me’ race and gender biases in human language production," Nature Human Behaviour, Nature, vol. 8(9), pages 1706-1715, September.
    4. Clotilde Napp, 2023. "Gender stereotypes embedded in natural language are stronger in more economically developed and individualistic countries," Post-Print hal-04316389, HAL.
    5. Adrián Mateo-Orcajada & Lucía Abenza-Cano & Raquel Vaquero-Cristóbal & Sonia M. Martínez-Castro & Alejandro Leiva-Arcas & Ana María Gallardo-Guerrero & Antonio Sánchez-Pato, 2021. "Gender Stereotypes among Teachers and Trainers Working with Adolescents," IJERPH, MDPI, vol. 18(24), pages 1-10, December.
    6. Elena De Gioannis, 2024. "On the association between gender-science stereotypes’ endorsement and gender bias attribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3087-3106, August.

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