Force versus fury: Sex differences in the relationships among physical and psychological threat potential, the facial width‐to‐height ratio, and judgements of aggressiveness
Bibliographic record
Abstract
Individuals with larger facial width-to-height ratios (FWHRs) are judged as more threatening, and engage in more threat-related behavior, than do individuals with smaller FWHRs. Here we identified components of threat potential that are related to the FWHR. In Study 1, the FWHR was correlated positively with physical threat potential (bicep size) in women and with both physical and psychological (anger proneness) threat potential in men. Behavioral aggression was measured in a subset of these participants using the Point Subtraction Aggression Paradigm (costly aggression) and a Money Allocation Task (non-costly aggression). Psychological (but not physical) threat potential predicted non-costly aggression and physical (but not psychological) threat potential predicted costly aggression. In Study 2, a separate set of participants judged the anger proneness, strength, or aggressiveness of male participants photographed in Study 1. Participants' judgements of all three characteristics were associated with the FWHR, and there were sex differences in how aggressiveness was conceptualized (for women, aggressiveness was associated with anger proneness, for men, aggressiveness was associated with strength). These results are consistent with the hypothesis that the FWHR may be an adaptation to cue the threat potential of men.
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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 | 0.002 |
| 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) | 0.003 | 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".