Income Inequality and Bullying Victimization and Perpetration: Evidence From Adolescents in the COMPASS Study
Bibliographic record
Abstract
Previous research indicates that the disproportionate distribution of income within society is associated with aggression and violence. Although research has been conducted identifying the relationship between income inequality and bullying victimization and perpetration, little is known about possible mediators. We investigated the association between income inequality and bullying perpetration and victimization among adolescents participating in the Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary behavior (COMPASS) study. We identified whether school connectedness and psychosocial well-being mediated the relationship between income inequality and bullying behavior. This study used pooled cross-sectional data from 147,748 adolescents aged 13 to 18 from three waves (2015-2016, 2016-2017, 2017-2018) of the COMPASS study from 157 secondary schools in British Columbia, Alberta, Ontario, and Quebec (Canada). The Gini coefficient was calculated based on the school Census Divisions (CD) using the Canada 2016 Census and linked with student data. We used multilevel modeling to investigate the relationship between income inequality and self-reported bullying victimization and perpetration, while controlling for individual-, school-, and CD-level characteristics. A standard deviation increase in Gini coefficient was associated with increased odds for bullying victimization and perpetration. Findings were observed among girls; however, inequality was only associated with perpetration among boys. We identified social cohesion and psychosocial well-being as potential mediators. To counter the adverse effects of income inequality, school-based interventions designed to increase school connectedness and student psychosocial well-being should be implemented to protect against bullying.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".