Meta‐analytic results of ethnic group differences in peer victimization
Bibliographic record
Abstract
Research on the prevalence of peer victimization across ethnicities indicates that no one group is consistently at higher risk. In the present two meta-analyses representing 692,548 children and adolescents (age 6-18 years), we examined ethnic group differences in peer victimization at school by including studies with (a) ethnic majority-minority group comparisons (k = 24), and (b) White and Black, Hispanic, Asian, and Aboriginal comparisons (k = 81). Methodological moderating effects (measure type, definition of bullying, publication type and year, age, and country) were examined in both analyses. Using Cohen's d, results indicated a null effect size for the ethnic majority-minority group comparison. Moderator analyses indicated that ethnic majority youth experienced more peer victimization than ethnic minorities in the US (d = .23). The analysis on multiple group comparisons between White and Black (d = .02), Hispanic (d = .08), Asian (d = .05), Aboriginal (d = -.02) and Biracial (d = -.05) groups indicated small effect sizes. Overall, results from the main and moderator analyses yielded small effects of ethnicity, suggesting that ethnicity assessed as a demographic variable is not an adequate indicator for addressing ethnic group differences in peer victimization. Although few notable differences were found between White and non-White groups regarding rates of peer victimization, certain societal and methodological limitations in the assessment of peer victimization may underestimate differences between ethnicities. Aggr. Behav. Aggr. Behav. 42:149-170, 2015. © 2014 Wiley Periodicals, Inc.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".