Developmental trajectories of peer-reported aggressive behavior: The role of friendship understanding, friendship quality, and friends’ aggressive behavior.
Bibliographic record
Abstract
OBJECTIVE: To investigate developmental trajectories in peer-reported aggressive behavior across the transition from elementary-to-middle school, and whether aggressive behavior trajectories were associated with friendship quality, friends' aggressive behavior, and the ways in which children think about their friendships. METHOD: grade). Peer nominations were used to assess the target child's and friend's aggressive behavior. Self- and friend reports were used to measure friendship quality; friendship understanding was assessed via a structured interview. RESULTS: General Growth Mixture Modeling (GGMM) revealed three distinct trajectories of peer-reported aggressive behavior across the school transition: low-stable, decreasing, and increasing. Adolescents' understanding of friendship formation differentiated the decreasing from the low-stable aggressive behavior trajectories, and the understanding of friendship trust differentiated the increasing from the low-stable aggressive and decreasing aggressive behavior trajectories. CONCLUSIONS: The findings indicated that a sophisticated understanding of friendship may serve as a protective factor for initially aggressive adolescents as they transition into middle school. Promoting a deepened understanding of friendship relations and their role in one's own and others' well-being may serve as an important prevention and intervention strategy to reduce aggressive behavior.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".