Peer Influence and Selection Impact on Adolescent Aggression: Exploring Nonaggressive Delinquency, Peer Victimization, and Moral Disengagement
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of nonaggressive delinquency, victimization, and moral disengagement on aggression at both the individual and social influence levels. METHODS: We collected two consecutive rounds of longitudinal data, with a 6-month interval, from seven high schools in the central region of China in 2016, comprising a total of 2406 valid samples. The Stochastic Actor-Oriented Models (SAOMs), a dynamic network analysis method is used explore the effect in individual and social influence levels. RESULTS: The main findings are as follows: (1) At the individual level, we found that nonaggressive delinquency and moral disengagement were significantly positively associated with proactive aggression, while victimization was significantly related to proactive aggression but not reactive aggression. (2) At the social influence level, our findings highlighted the presence of a selection effect in adolescent friendships. Specifically, adolescents were more likely to form friendships with peers of the same gender, socioeconomic status (SES), and similar levels of nonaggressive delinquency and moral disengagement. (3) Regarding friends' negative behaviors and attitudes, friends' moral disengagement and peer victimization were not significantly associated with individual levels of proactive and reactive aggression. However, friends' nonaggressive delinquency had a significant negative association with adolescents' reactive aggression, while no significant association was found with proactive aggression. CONCLUSION: This study used SAOMs to examine how individual and social factors influence adolescent aggression, finding that individual delinquency and moral disengagement significantly associated with aggression. While friends' victimization and moral disengagement had no direct effects, friends' delinquency reduced reactive aggression, and friends' overall aggression increased individual aggression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".