Are we accepting enough? Examining the association between homophobia and acceptance of diversity
Bibliographic record
Abstract
Peer victimization is a serious public health problem and Dan Olweus, pioneer in bullying research, has long recognized school safety as a fundamental human right. The purpose of this study was to assess and address a facet of a larger and more complex critique on issues related to Equity, Diversity, and Inclusion in schools. As such, the present study examined how peer and adult acceptance of sexual diversity is associated with homophobic perpetration and peer victimization among high-school students. Two hundred nineteen participants (61.3% boys, M age = 14.82, SD = 1.31) across high schools in Southern Quebec, Canada, completed questionnaires assessing adult acceptance of sexual orientation and reports of homophobic perpetration and victimization among students. Results from path analyses revealed that adult acceptance of sexual diversity significantly predicted homophobic perpetration, β = −0.221, t (9) = −2.68, p = .002, and victimization, β = −0.193, t (9) = −3.05, p = .007, while peers’ acceptance of sexual diversity did not. The findings highlight the importance of adult acceptance of sexual diversity in reducing peer victimization, emphasizing the need for a discrimination-free school climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".