Taking Control of Aggression: Perceptions of Aggression Suppress the Link between Perceptions of Facial Masculinity and Attractiveness
Bibliographic record
Abstract
Women's preferences for masculine-looking male faces are inconsistent across studies, with some studies finding a positive relationship between masculinity and attractiveness and others finding a negative relationship or no association. One possible reason for this inconsistency is that the perception of masculinity is also associated with perceptions of aggression, which may be viewed as particularly costly to women (aggressive individuals are more likely to experience injury or death). Based on the proposal that women's preference for masculinity is in conflict with their aversion for aggression in male faces, we hypothesized that the bivariate associations between perceptions of masculinity and attractiveness would be weak or negative, but would be positive and significantly stronger after controlling statistically for perceptions of aggression. Across three studies involving three sets of faces (n = 25, 54, 24) and five sets of raters (n = 29, 30, 26, 16, 10), this hypothesis was supported with the average correlation between perceptions of masculinity and attractiveness (r = -.09) reversing in direction and substantially increasing in magnitude after perceptions of aggression were controlled statistically (r = .35). Perceived masculinity may thus involve both attractive and unattractive components, and women's preferences for masculinity may involve weighing its relative costs and benefits.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".