Interpersonal sporting relationships as experienced by LGBTQ+ athletes
Bibliographic record
Abstract
Despite concerted efforts, inclusivity is not equally distributed across all sporting contexts. The perceived ex/inclusivity of sporting environments is often influenced by interpersonal relationships, this is especially true for LGBTQ+ athletes. Using an online cross-sectional survey, LGBTQ+adults (N = 741) were asked the following open-ended response question, would you describe your past and/or current relationships with teammates, coaches, and other sports-related support staff? The resulting texts were independently coded by two researchers using thematic analysis and compared. All discrepancies were discussed with and rectified by a third researcher who acted as a critical peer. LGBTQ+ athletes described how their interpersonal relationships created perceived sporting environments that existed on a continuum ranging from exclusive to inclusive. Exclusive sporting environments were characterized by experiences of discrimination fuelled by conceptions of ability, aesthetics, and homo/transphobia. Formal or neutral sporting environments were maintained through self-distancing techniques and inclusive sporting environments were defined by strong positive relationships with coaches and teammates that were primarily based on acceptance. Both higher athletic ability and identity concealment strategies created increased opportunity for mobility along the sporting environment continuum. This study demonstrates that despite strides towards inclusivity within sport, there are still pervasive vestiges of intolerance that need to be engaged with and deconstructed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".