Asian Men and Black Women Hold Weaker Race–Gender Associations: Evidence From the United States and China
Bibliographic record
Abstract
Prior work finds a consistent association between race and gender: People associate Asian with female and Black with male. We used mouse-tracking to examine whether different U.S. racial/ethnic groups hold this same association (Study 1) and compared Asian-American participants to ethnically Chinese participants in China (Study 2). In Study 1, White and Hispanic participants showed the expected “race is gendered” effect, and the strength of the effect did not differ between men and women. However, participants with a counter-stereotypical racial-gender identity (Black women and Asian men) showed weaker race–gender associations. The same pattern emerged for East Asian participants in Study 2, both among people living in the United States and China. These data provide the first evidence of moderation in Asian-female, Black-male associations and further reveal the importance of considering intersectional identities in social cognition and social perception.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".