Links between friends’ physical aggression and adolescents’ physical aggression
Bibliographic record
Abstract
Exposure to deviant friends has been found to be a powerful source of influence on children’s and adolescents’ aggressive behavior. However, the contribution of deviant friends may have been overestimated because of a possible non-accounted gene-environment correlation (rGE). In this study, we used a cross-lagged design to test whether friends’ physical aggression at age 10 predicts an increase in participants’ physical aggression from age 10 to age 13 years. Participants were 201 pairs of monozygotic twins who are part of the Quebec Longitudinal Twin Study. We performed two sets of analyses. In the first set of analyses, using twins as singletons, we found that teacher-rated friends’ physical aggression predicted an increase in each twin’s self-reported physical aggression from age 10 to age 13, above and beyond auto-regressive and concurrent links. Second, we used within-pair differences in regard to friends’ physical aggression to predict an increase in within-pair differences in physical aggression, thus accounting for family-wide influences, including a likely rGE at age 10. No significant association was found, however. These results suggest that part of the influence attributed to friends in past studies may have been due to common underlying genetic effects on both physical aggression and association with physically aggressive friends.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".