Emotion and Gender Typicality Cue Sexual Orientation Differently in Women and Men
Bibliographic record
Abstract
Heterosexual individuals tend to look and act more typical for their gender compared to gay and lesbian individuals, and people use this information to infer sexual orientation. Consistent with stereotypes associating happy expressions with femininity, previous work found that gay men displayed more happiness than straight men-a difference that perceivers used, independent of gender typicality, to judge sexual orientation. Here, we extended this to judgments of women's sexual orientation. Like the gender-inversion stereotypes applied to men, participants perceived women's faces manipulated to look angry as more likely to be lesbians; however, emotional expressions largely did not distinguish the faces of actual lesbian and straight women. Compared to men's faces, women's faces varied less in their emotional expression (appearing invariably positive) but varied more in gender typicality. These differences align with gender role expectations requiring the expression of positive emotion by women and prohibiting the expression of femininity by men. More important, greater variance within gender typicality and emotion facilitates their respective utility for distinguishing sexual orientation from facial appearance. These findings thus provide the first evidence for contrasting cues to women's and men's sexual orientation and suggest that gender norms may uniquely shape how men and women reveal their sexual orientation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".