Unpacking the misfit effect: Exploring the influence of gender and social norms on the association between aggression and peer victimization
Bibliographic record
Abstract
Social norms are vital for the functioning of adolescent peer groups; they can protect the well-being of groups and individual members, often by deterring harmful behaviors, such as aggression, through enforcement mechanisms like peer victimization; in adolescent peer groups, those who violate aggression norms are often subject to victimization. However, adolescents are nested within several levels of peer group contexts, ranging from small proximal groups, to larger distal groups, and social norms operate within each. This study assessed whether there are differences in the enforcement of aggression norms at different levels. Self-report and peer-nomination data were collected four times over the course of a school year from 1,454 early adolescents ( M age = 10.27; 53.9% boys) from Bogota, Colombia. Multilevel modeling provided support for social regulation of both physical aggression and relational aggression via peer victimization, as a function of gender, grade-level, proximal (friend) or distal (class) injunctive norms of aggression (perceptions of group-level attitudes), and descriptive norms of aggression. Overall, violation of proximal norms appears to be more powerfully enforced by adolescent peer groups. The findings are framed within an ecological systems theory of adolescent peer relationships.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".