Homo- and transnegativity in sport in Europe: Experiences of LGBT+ individuals in various sport settings
Bibliographic record
Abstract
There is broad academic consensus that LGBT+ individuals have been marginalised in both sporting culture and in the academic literature. While the majority of academic research is conducted in the USA, UK, Canada and Australia, the present research is the first to provide a comprehensive picture of the situation and experiences of LGBT+ individuals in sport in Europe based on a quantitative online survey with LGBT+ respondents over 16 years old ( N = 5524). Against the background of a multilevel model for understanding the experiences of LGBT+ individuals and the minority stress model, this article focuses on two questions: firstly, if, and to what extent, LGBT+ individuals witness or experience homo-/transnegative episodes in sport and, secondly, whether they refrain from participating in sport and/or feel excluded from specific sports due to their sexual orientation and/or gender identity. The analysis takes into account diverse intersections of sexual orientation and gender identities within the umbrella of LGBT+ and different sport contexts that reflect the broad scope of sport cultures. Data reveal that non-cisgender persons make up the most vulnerable group within the umbrella of LGBT+ and that there is an inverse relation of distal/proximal stressors with regard to experiences of homophobic language in different sport contexts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".