Analyzing representations of equity, diversity, and inclusion on Ontario University Athletics Schools’ websites through a critical race framework
Bibliographic record
Abstract
The objective of this paper is to identify and analyze the use of equity, diversity, and inclusion (EDI) language amongst Ontario University Athletics’ (OUA) member-institutions through a content analysis of their websites. We specifically looked at how EDI is positioned in relation to the inclusion, mission, and vision statements of each institution as well as within their values, objectives, policies, and committees. We identified two main findings: (1) that relatively few websites had EDI-related statements and/or content, and the language used tends to reproduce problematic discourses that continue to perpetuate systemic inequities and feelings of erasure and denial of BIPOC student-athletes; and (2) there is limited representation of tangible EDI initiatives/outcomes on these websites. Based on our findings, we argue that while OUA institutions have taken tangible steps towards achieving EDI objectives, the underlying culture of whiteness in these institutions still needs to be addressed for meaningful systemic change that addresses the needs of equity-owed groups. We further demonstrate that disproportionate and varying attention exists between high-performance sport and EDI-based work, evidenced through liberalized forms of anti-racism that informs this work, and the racialized labor it accrues for Black, Indigenous, and People of Color (BIPOC) students/employees. Overall, our findings add to a growing body of literature that acknowledges the limitations of post-secondary institutions’ initiatives to address their structure, culture, and role in perpetuating inequity in 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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".