Equity-Deserving Groups, Sport, and Recreation in Calgary: An Analysis of Equity, Diversity, Inclusion, and Accessibility-Related Policies
Bibliographic record
Abstract
Equity, diversity, inclusion, and accessibility (EDIA) policies play a crucial role in addressing the many reported barriers equity-deserving groups experience in relation to Canadian sport and recreation. Recognizing this, I examined the publicly available EDIA-related policies and statements of 76 multi-sport facilities across Calgary, Alberta, Canada. Drawing on Kimberlé Crenshaw’s theory of intersectionality, an intersectional anti-oppression framework, and relevant empirical literature, I conducted a critical discourse analysis on the statements and policies found. My analysis led to the creation of three themes for statements: (1) all are welcome, (2) some are welcome, and (3) land acknowledgements, and four themes for policies: (1) codes of conducts, (2) zero tolerance policies, (3) targeted policies, and (4) bylaws. Most statements and policies fell far short of being effective or impactful for equity-deserving groups, particularly (but not only) because they were often absent of specificity and actionable items while also failing to address intersecting systems of oppression. My findings underscore the need for more comprehensive and intersectional anti-oppression EDIA policies in sport and recreational facilities to address systemic inequities and foster true inclusivity.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".