A cross-cultural analysis of the relations of physical and relational aggression with peer victimization
Bibliographic record
Abstract
To better address the many consequences of peer victimization, research must identify not only aspects of individuals that put them at risk for victimization, such as aggression, but also aspects of the context that influence the extent of that risk. To this end, this study examined the contextual influences of gender, same-sex peer group norms of physical and relational aggression, and nationality on the associations of physical and relational aggression with peer victimization in early adolescents from Canada, China, Brazil, and Colombia ( N = 865; M age = 11.01, SD = 1.24; 55% boys). Structural equation modeling was used to test for measurement invariance of the latent constructs. Multilevel modeling revealed that both forms of aggression were positive predictors of peer victimization, but physical aggression was a stronger predictor for girls than boys. Cross-national differences emerged in levels of peer victimization, such that levels were highest in Brazil and lowest in Colombia. Cross-national differences were also evidenced in the relationship between relational aggression and victimization: the relationship was positive in China, Brazil, and Canada (listed in descending order of magnitude), but negative in Colombia. Above and beyond the cross-national differences, physical aggression was a stronger predictor of victimization in peer groups low in physical aggression, and relational aggression was a stronger predictor in peer groups low in relational aggression. Ultimately, this research is intended to contribute to a better theoretical understanding of risk factors for peer victimization and the development of more effective and culturally-appropriate prevention and intervention efforts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".