Romantic love and involvement in bullying and cyberbullying in 15-year-old adolescents from eight European countries and regions
Bibliographic record
Abstract
Sexual minority youth are at greater risk for bullying victimization than their heterosexual peers but data on perpetration and cybervictimization is limited. Using representative data from seven European countries and one region (N = 14,545), this study compared traditional bullying victimization and perpetration, and cyberbullying victimization among 15-year-old adolescents who reported ever being in love with same- or both- gender peers (sexual minority) versus opposite-gender peers (non-minority). Adolescents who have never been in love and non-respondents were also included. Analyses were stratified by gender and adjusted for country/region and family affluence. Compared to those attracted to opposite-gender peers, traditional bullying perpetration was more likely to be reported by both-gender attracted girls, while bullying victimization was more likely to be reported by both-gender attracted girls and both- and same-gender attracted boys. All sexual minority youth were more likely to report cybervictimization compared to their non-minority peers. Adolescents who have never been in love reported lower levels of bullying involvement than all other youth. Sexual minority stigma may contribute to higher risk of bullying involvement among adolescents. Interventions need to specifically address bullying involvement and associated health risks of sexual minority youth. Available evidence shows that explicit school policies and interventions tailored to local settings are particularly effective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".