The Relationship Between ‘Coming Out’ as Lesbian, Gay, or Bisexual and Experiences of Homophobic Behaviour in Youth Team Sports
Bibliographic record
Abstract
Abstract Introduction Homophobia appears to be greater in sport settings than in others. However, little is known about whether lesbian, gay or bisexual (LGB) young people experience discriminatory behaviour in team sports because of their sexuality and whether coming out to sport teammates is associated with homophobic behaviour. Method This study used a sample ( N = 1173; 15–21 years; collected in 2014–2015) from six countries (United States, United Kingdom, Canada, Australia, New Zealand, Ireland) to examine whether LGB youth who ‘come out’ to teammates experience homophobic behaviour. Results Close to half of the sample (41.6%) reported having been the target of homophobic behaviour (e.g. verbal slurs, bullying, assaults). Multivariate logistic regression models adjusting for age, gender, country and contact sport participation found that participants who ‘came out’ as being LGB to sports teammates were significantly more likely to report being a target of homophobic behaviour. There appeared to be a dose response with coming out to more people associated with a greater likelihood of experiencing homophobic behaviour. Conclusion The study results suggest a relationship between coming out as LGB and encountering homophobic behaviour in team sports. LGB experiences of homophobic behaviour appear common overall in this sample, but are greater in those who have come out to teammates. Policy Implication Sports administrators and governments need to develop programs and enforce policies that create safe sports environments where LGB youth can participate without encountering homophobic behaviour.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".