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Record W7128709289 · doi:10.7202/1123300ar

What is Safe about Safe Sport? A Gendered Interrogation of Canadian University Safe Sport Policies in Higher Education

2025· article· en· W7128709289 on OpenAlexaffvenueabout
Hayley Baker, Kasey Egan

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsWestern University
Fundersnot available
KeywordsInterrogationHigher educationBest practicePoison controlHuman factors and ergonomicsSuicide preventionOccupational safety and healthSport management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.330
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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