School ethnic composition and bullying in Canadian schools
Bibliographic record
Abstract
Bullying in ethnically diverse schools varies as a function of the ethnic composition and degree of diversity in schools. Although Canada is highly multicultural, few researchers have focused on the role of context on ethnic majority and minority youths’ bullying involvement. In the present study, 11,649 European-Canadian/ethnic majority (77%) and non-European Canadian/ethnic minority (23%) students in Grade 4 to Grade 12 completed an online Safe Schools Survey on general, physical, verbal, social, and cyber bullying. Hierarchical Linear Modeling (HLM) analyses indicated significant interactions between the proportion of non-European Canadian children in a school (Level 2) and individual ethnicity (Level 1) across most types of bullying victimization. Non-European Canadian students experienced less peer victimization in schools with higher proportions of non-European Canadian students, but ethnic composition was not related to European Canadian students’ peer victimization. No differences in bullying perpetration were found as a function of school ethnic composition across groups. Our findings suggest that ethnic composition in Canadian schools may not be strongly associated with bullying perpetration and that a higher representation of other ethnic minority peers may act as a buffer against peer victimization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".