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Intersectional race-gender stereotypes in natural language
Han-Wu-Shuang Bao1, Peter Gries1
1Manchester China Institute, University of Manchester, Manchester, UK.
The British Journal of Social Psychology
|April 24, 2024
Summary
This study reveals nuanced racial and gender stereotypes for Asian and Black men and women. Findings show Asian men are seen as less masculine, while both Asian men and women are perceived as more intelligent but less assertive than Black individuals.
Area of Science:
- Social Psychology
- Computational Linguistics
- Sociology
Background:
- Existing research on racial and gender stereotypes presents mixed findings.
- Understanding these stereotypes is crucial for addressing societal biases.
Purpose of the Study:
- To explore the gender, physical strength, warmth, and competence stereotypes associated with Asian and Black men and women.
- To analyze how these stereotypes are represented in natural language using advanced computational methods.
Main Methods:
- Utilized BERT language models pre-trained on diverse text data (books, news, Wikipedia, Reddit, Twitter).
- Employed the Fill-Mask Association Test (FMAT) to measure natural language propositions related to stereotypes.
- Examined stereotypes concerning masculinity-femininity, physical strength, warmth, and competence.
Main Results:
- Asian men are stereotyped as less masculine and less moral/trustworthy than Black men.
- Both Asian men and women are perceived as less muscular/athletic and less assertive/dominant compared to Black men and women, respectively.
- Asian individuals (men and women) are stereotyped as more sociable/friendly and more capable/intelligent than Black individuals.
Conclusions:
- Stereotypes of Asian and Black individuals are multifaceted, with significant gender nuances.
- A balanced perspective integrating gender schema theory and stereotype content model is necessary.
- Large language models can reveal how intersectional race-gender stereotypes are naturally expressed.
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