Sexual Orientation Perception Involves Gendered Facial Cues
Bibliographic record
Abstract
Perceivers can accurately judge a face's sexual orientation, but the perceptual mechanisms mediating this remain obscure. The authors hypothesized that stereotypes casting gays and lesbians as gender "inverts," in cultural circulation for a century and a half, lead perceivers to use gendered facial cues to infer sexual orientation. Using computer-generated faces, Study 1 showed that as two facial dimensions (shape and texture) became more gender inverted, targets were more likely to be judged as gay or lesbian. Study 2 showed that real faces appearing more gender inverted were more likely to be judged as gay or lesbian. Furthermore, the stereotypic use of gendered cues influenced the accurate judgment of sexual orientation. Although using gendered cues increased the accuracy of sexual orientation judgments overall, Study 3 showed that judgments were reliably mistaken for targets that countered stereotypes. Together, the findings demonstrate that perceivers utilize gendered facial cues to glean another's sexual orientation, and this influences the accuracy or error of judgments.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".