What is Safe about Safe Sport? A Gendered Interrogation of Canadian University Safe Sport Policies in Higher Education
Bibliographic record
Abstract
In this paper, we seek to examine how organizational norms and structures in Canadian university sport perpetuate a culture that does not adequately address maltreatment, harassment, and abuse that affects women athletes. Our examination focuses on two inter-related questions. First, how do safe sport policies highlight gendering practices in university athletics? And second, how is safety problematized in safe sport policies? In addressing our research question, we apply genderwashing as a conceptual framework, alongside Carol Bacchi’s “What’s the Problem” approach to analyze the USPORTS and Western University’s safe sport policies. Our analysis reveals that safe sport policies demonstrate genderwashing practices in their use of gender-neutral language. The use of gender-neutral language within these policies contributes to an erasure of women athletes’ experiences, and a reluctance to engage with issues of gender violence in sport. We suggest addressing this issue will require a commitment to creating a safe sport culture that recognizes gender, and other axis of identity, as relevant to athletes’ diverse experiences and understanding of safety within Canadian university sport.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.048 | 0.036 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".