Emotional arousal and gender differences in aggression: A meta‐analysis
Bibliographic record
Abstract
Abstract This meta‐analysis investigated the possibility that gender differences in aggression, and the variability in these differences, are a function of gender differences in the regulation of arousal generated in emotionally evocative contexts. The sample of studies for this analysis was based on an exhaustive search of the relevant research reports from 1965–1999. Studies were excluded from the sample if they were case studies; investigated spousal/familial or societal violence, war, suicide, or political violence; involved clinical or deviant participants; included fewer than 10 participants; included all male, all female, all non‐Caucasian, or non‐US/non‐Canadian participants. Based on previous evidence that males may be more easily aroused by aggressive‐relevant emotional stimuli than females, and that males may have more difficulty regulating emotionally arousing states than females, we hypothesized that the magnitude of the gender differences in aggression would covary, in a nonlinear manner, with the emotional evocativeness of the study context. Consistent with our hypothesis, the magnitude of gender differences in aggression was relatively small in research contexts that appeared to produce no or large increments in emotional arousal and larger (favoring males) in contexts that appeared to produce small or medium increments in emotional arousal. Aggr. Behav. 28:366–393, 2002. © 2002 Wiley‐Liss, Inc.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".