Child Exposure to Violent Content and Aggression: A Novel Approach to an Old Debate
Bibliographic record
Abstract
OBJECTIVE: Our objective is to examine bidirectional, within-person associations between early childhood exposure to violent content in boys and girls and the development of reactive and proactive aggression. METHODS: Data are from 975 girls and 987 boys from Quebec, Canada, followed in the context of the Quebec Longitudinal Study of Child Development (1998-2023). Parents reported child exposure to violent TV content and proactive and reactive aggression at ages 4 to 6. Data were analyzed using random-intercept cross-lagged panel models. RESULTS: Greater exposure to violent content at ages 4 was associated with within-person increases in reactive aggression by age 5 in boys (β = 0.16, 95% Confidence Interval = [0.050, 0.261]) and girls (β = 0.13, CI = [0.004, 0.229]). In addition, greater proactive aggression at age 4 was associated with a within-person decrease in exposure to violent content by age 5 in boys (β = -0.08, 95% CI = [-0.174, -0.003]) and girls (β = -0.09, 95% CI = [-0.174, -0.009]). A similar pattern was observed for boys and girls between the ages of 5 and 6 (β = -0.08, 95% CI = [-0.167, -0.003] for boys and β = -0.10, 95% CI = [-0.194, -0.010] for girls). CONCLUSIONS: Our findings suggest a positive association between early childhood exposure to violent content and the development of reactive aggression. Greater child proactive aggression was also associated with reduced exposure to violent content, suggesting that parents may adopt a reactive, rather than preventive approach when monitoring child media habits.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".