The Role of Race, Age, and Body Size in Perceptions of Femininity, Attractiveness, and Expressions of Benevolent Sexism
Bibliographic record
Abstract
Foundational research on gender stereotypes and prejudice often lacks target diversity, contributing to a possibly narrow understanding of person perception. To address this gap, five within-participants studies (N = 757) examined how perceptions of femininity, attractiveness, and expressions of benevolent sexism shift at intersections of target women’s race (Asian/Black/White), age (young/old), and body size (thin/fat). Studies 1a and 1b investigated femininity and attractiveness perceptions using category labels for racial groups (e.g., Asian women), combinations of race and age groups (e.g., young Asian women), and combinations of race and body size groups (e.g., thin Asian women). Among thin and young targets, Asian women and White women were rated as more feminine and more attractive than Black women. In contrast, racial differences attenuated among old and fat targets, suggesting that older age and larger body size may be stronger cues than race for femininity and attractiveness judgments. Studies 2a (race), 2b (race x age), and 2c (race x body size) replicated and extended these findings using pilot-tested photos of real women (four studies; N = 160). In Study 2a, participants rated photos of three randomized targets (Asian/Black/White) on femininity, attractiveness, and benevolent sexism. Asian women and White women were perceived as more feminine and more attractive than Black women, but were only marginally greater targets of benevolent sexism than Black women. In Studies 2b and 2c, participants rated photos of six randomized targets varying in race and age and race and body size. Race interacted with age (2b) and body size (2c), such that Asian women and White women were perceived as more feminine and more attractive than Black women when targets were young or thin. Young and thin White women, along with young Asian women, were greater targets of benevolent sexism than young and thin Black women. Racial differences attenuated among old and fat targets for femininity, attractiveness, and benevolent sexism. Together, these data update our understanding of gender stereotypes and prejudice, and further demonstrate that person perception shifts substantially at different intersections of identity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".