Gender Differences in Bullying Reflect Societal Gender Inequality: A Multilevel Study With Adolescents in 46 Countries
Bibliographic record
Abstract
PURPOSE: Social patterns in bullying show consistent gender differences in adolescent perpetration and victimization with large cross-national variations. Previous research shows associations between societal gender inequality and gender differences in some violent behaviors in adolescents. Therefore, there is a need to go beyond individual associations and use a more social ecological perspective when examining gender differences in bullying behaviors. The aim of the present study was twofold: (1) to explore cross-national gender differences in bullying behaviors and (2) to examine whether national-level gender inequality relates to gender differences in adolescent bullying behaviors. METHODS: Traditional bullying and cyberbullying were measured in 11-year-olds to 15-year-olds in the 2017/18 Health Behaviour in School-aged Children study (n = 200,423). We linked individual data to national gender inequality (Gender Inequality Index, 2018) in 46 countries and tested their association using mixed-effects (multilevel) logistic regression models. RESULTS: Large cross-national variations were observed in gender differences in bullying. Boys had higher odds of perpetrating both traditional and cyberbullying and victimization by traditional bullying than girls. Greater gender inequality at country level was associated with heightened gender differences in traditional bullying. In contrast, lower gender inequality was associated with larger gender differences for cyber victimization. DISCUSSION: Societal gender inequality relates to adolescents' involvement in bullying and gendered patterns in bullying. Public health policy should target societal factors that have an impact on young people's behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".