Does war beget child aggression? Military violence, gender, age and aggressive behavior in two Palestinian samples
Bibliographic record
Abstract
We examined, first, the relations between children's exposure to military violence and their aggressive behavior and the role of age and gender in that relation in two Palestinian samples. Second, we tested parenting practices as a moderator of the relation between exposure to military violence and aggressive behavior, and third, whether exposure to military violence of different nature (direct victimization versus witnessing) has specific associations with different forms of aggression (reactive, proactive and aggression-enjoyment). Study I was conducted in a relatively calm military-political atmosphere in Palestine-Gaza, and included 640 children, aged 6-16 years whose parents (N=622) and teachers (N=457) provided reports. Older children (> or =12 years) provided self-reports (N=211). Study II included 225 Palestinian children aged 10-14-year, who participated during a high-violence period of the Al Aqsa Intifada characterized by air raids, killing and destruction. Results showed that witnessing severe military violence was associated with children's aggressive and antisocial behavior (parent-reported) in study I, and with proactive, reactive and aggression-enjoyment (child-reported) in the study II. As hypothesized, good and supporting parenting practices could moderate the link between exposure to military violence and aggressive behavior. Aggr.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".