Facing Aggression: Cues Differ for Female versus Male Faces
Bibliographic record
Abstract
The facial width-to-height ratio (face ratio), is a sexually dimorphic metric associated with actual aggression in men and with observers' judgements of aggression in male faces. Here, we sought to determine if observers' judgements of aggression were associated with the face ratio in female faces. In three studies, participants rated photographs of female and male faces on aggression, femininity, masculinity, attractiveness, and nurturing. In Studies 1 and 2, for female and male faces, judgements of aggression were associated with the face ratio even when other cues in the face related to masculinity were controlled statistically. Nevertheless, correlations between the face ratio and judgements of aggression were smaller for female than for male faces (F(1,36) = 7.43, p = 0.01). In Study 1, there was no significant relationship between judgements of femininity and of aggression in female faces. In Study 2, the association between judgements of masculinity and aggression was weaker in female faces than for male faces in Study 1. The weaker association in female faces may be because aggression and masculinity are stereotypically male traits. Thus, in Study 3, observers rated faces on nurturing (a stereotypically female trait) and on femininity. Judgements of nurturing were associated with femininity (positively) and masculinity (negatively) ratings in both female and male faces. In summary, the perception of aggression differs in female versus male faces. The sex difference was not simply because aggression is a gendered construct; the relationships between masculinity/femininity and nurturing were similar for male and female faces even though nurturing is also a gendered construct. Masculinity and femininity ratings are not associated with aggression ratings nor with the face ratio for female faces. In contrast, all four variables are highly inter-correlated in male faces, likely because these cues in male faces serve as "honest signals".
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".