The effect of classroom aggression‐related peer group norms on students' short‐term trajectories of aggression
Bibliographic record
Abstract
= 10.2 years) from 63 fourth-, fifth- and sixth- grade classrooms in nine mixed-sex schools in Bogotá, Colombia, we examined whether growth trajectories of measures of overt and relational aggression varied as a function of classroom norms for aggression. Multilevel growth mixture modeling revealed (a) distinct trajectories of overt and relational aggression for boys and girls and (b) that norm salience (i.e., the process by which a group norm is made salient via the punishments or reinforcements to the behavior within the group) was a better predictor of associations with trajectories of overt and relational aggression than were perceived injunctive norms (i.e., the perceived standards of what is approved or disapproved in a social context). In classrooms where popular or accepted children were perceived by their peers as aggressive, more boys followed an increasing trajectory of overt and relational aggression than a low-stable trajectory, and more girls followed a high-stable trajectory of relational aggression than a low-stable trajectory. These findings are discussed in terms of the practical implications for the design of educational interventions aimed at preventing aggression in classroom settings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".