Aggression: Gender Differences in
Bibliographic record
Abstract
Abstract Increasingly, researchers are turning their attention to the issue of aggression and violence perpetrated by girls and women. This reflects mounting evidence from several related fields of research including: criminal justice, corrections, forensic psychology and psychiatry, domestic violence, child and elder abuse, and delinquency and juvenile justice. Together, this research supports several broad conclusions: (i) the assumption of female nonviolence is untenable; (ii) rates of female‐perpetrated aggression are escalating; (iii) aggression by females often has serious negative implications for victims; and (iv) there is insufficient research of risk and protective factors, developmental trajectories, and clinical programs to prevent and reduce female aggression. In this paper, we report the prevalence and incidence of aggression among females; document sex differences and similarities in aggressive and violent behavior; and examine sex‐specific versus common risk factors. We conclude with clinical implications for prevention and intervention and reflect on gaps in knowledge and directions for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".