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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".