Aggression Type Influences Perceptions of a Woman’s Body Size, Personality, and Behavior
Bibliographic record
Abstract
Although women engage in both physical and nonphysical aggression, little is known about how aggression type influences perceptions of their morphology, personality, and social behavior. Evolutionary theory predicts that women avoid physical aggression due to risk of injury, which could compromise reproductive success. Engaging in physical aggression might therefore decrease women's perceived mate value. However, physical aggression could be advantageous for some women, such as those who are larger in size and less vulnerable to injury. This presents the possibility that physically aggressive women might be perceived as larger and not necessarily lower in mate value. These hypotheses have not been tested. Across three studies, I used narratives to test the effect of aggression type (physical, verbal, indirect, nonaggressive) on perceptions of women's height, weight, masculinity, attractiveness, and social status. In Studies 1 and 2, participants perceived a physically aggressive woman to be both larger and more masculine than nonphysically aggressive women. In Study 3, participants perceived both a physically aggressive woman and a nonaggressive woman to be larger than an indirectly aggressive woman; the effect of aggression type on perceptions of a hypothetical man's height was not significant. I also found some evidence that aggression type influenced perceptions of attractiveness and social status, but these were small and inconsistent effects that warrant further study. Taken together, the results suggest that physical and indirect aggressive behavior may be associated with certain morphological and behavioral profiles in women.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| 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".