Bibliographic record
Abstract
The following dissertation sought to understand how sport stakeholders conceptualize and experience safe sport and to elicit their recommendations to advance safe sport, a movement that has emerged in response to cases of athlete maltreatment. To-date, the related literature indicates there is no universal definition of safe sport and thus, prevention and intervention initiatives differ; further, these initiatives are not necessarily empirically or theoretically driven. In Study 1, a constructivist grounded theory was employed, and semi-structured interviews were conducted with forty-three stakeholders in sport to elicit views of the meaning of the term safe sport. The findings revealed commonalities among the participants’ interpretations, specifically pertaining to the prevention of and intervention in incidences of physical, psychological, and sexual harm. Additionally, some participants’ interpretations expanded beyond the prevention of harm to include the optimisation of the sport experience, characterized by the promotion of positive values and human rights. In Study 2, an interpretive phenomenological analysis was used to explore equity-deserving athletes’ understanding and lived experiences of safe sport. Semi-structured interviews were conducted with seven athletes of diverse intersectional identities. The findings suggest that athletes from equity-deserving groups experience verbal and non-verbal forms of discrimination in sport and questioned whether safe sport was an attainable outcome for them. Moreover, athletes with visible, under-represented characteristics (e.g., Black, physical disability) perceived themselves as more vulnerable to unsafe sport experiences compared to athletes who could hide elements of their identity (e.g., gay athletes). Finally, Study 3 was a constructivist grounded theory that utilized semi-structured interviews to explore thirteen sport administrators’ perspectives of advancing safe sport. The participants recommended that sport organisations establish a universal framework of safe sport, design and implement education, implement and enforce policies, establish independent monitoring and complaint mechanisms, and conduct research to ensure advancement strategies are current and applicable. The current dissertation contributes to the growing body of safe sport literature by recommending that conceptualizations and advancement strategies of safe sport, which tend to be focused on the prevention of harm, extend to the promotion of human rights in sport through safeguarding.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| 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".