Sexual orientation trends and disparities in school bullying and violence-related experiences, 1999–2013.
Bibliographic record
Abstract
Numerous recent studies have demonstrated that schools are often unsafe for lesbian, gay, and bisexual (LGB) adolescents, who are more likely than heterosexual peers to be bullied, harassed, or victimized in school contexts. Virtually all of these studies call for change, yet none investigate whether or not it has occurred. Using repeated waves of a population-based high school survey, we examine (1) the extent to which sexual orientation differences in school bullying and violence-related experiences are reported by lesbian/gay, bisexual, and heterosexual male and female adolescents; (2) trends in school bullying and violence-related experiences for each gender/orientation group, and (3) whether disparities have changed over time. Data were drawn from eight Massachusetts biennial Youth Risk Behavior Surveys from 1999 to 2013, grouped into 4 waves totaling 24,845 self-identified heterosexual, 270 lesbian/gay, and 857 bisexual youth. Disparities between LGB and heterosexual peers were found in all indicators. Heterosexual youth and gay males saw significant reductions in every outcome between the first and last waves. Among bisexual males, skipping school due to feeling unsafe, carrying weapons in school, and being bullied all decreased, but among lesbians and bisexual females only fighting in school declined significantly. Improvement trends in school safety were more consistent for heterosexual youth and gay males than for bisexual or lesbian females. Notably, despite these improvements, almost no reduction was seen in sexual orientation disparities. Future research should identify influences leading to reduced school victimization, especially focusing on ways of eliminating persistent sexual orientation disparities. Future research should identify influences leading to reduced school victimization, especially focusing on ways of eliminating persistent sexual orientation disparities.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".