‘Safe Sport Is Not for Everyone’: Equity-Deserving Athletes’ Perspectives of, Experiences and Recommendations for Safe Sport
Bibliographic record
Abstract
There is a growing concern that the voices of athletes, and in particular, athletes from equity-deserving groups, are unaccounted for in the development and advancement of Safe Sport initiatives. The lack of consideration of the needs and experiences of diverse groups is concerning, given the existing literature outside the context of sport indicating that equity-deserving individuals experience more violence. As such, the following study sought to understand how equity-deserving athletes interpret and experience Safe Sport. Grounded within an interpretive phenomenological analysis, semi-structured interviews were used to understand how athletes with marginalised identities conceptualise and experience Safe Sport. Seven participants, including two Black male athletes, two White, gay male athletes, one Middle Eastern female athlete, one White, female athlete with a physical disability and one White, non-binary, queer, athlete with a physical disability, were asked to conceptualise and describe their experiences of Safe Sport. The findings revealed these athletes perceived Safe Sport as an unrealistic and unattainable ideal that cannot fully be experienced by those from equity-deserving groups. This interpretation was reinforced by reported experiences of discriminatory comments, discriminatory behaviours and systemic barriers, perpetrated by coaches, teammates, and resulting from structural aspects of sport. The findings draw on the human rights literature to suggest integrating principles of equity, diversity and inclusion are fundamental to safeguarding equity-deserving athletes.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".