An intersectional Foucauldian analysis of Canadian national sport organisations’ ‘equity, diversity, and inclusion’ (EDI) policies and the reinscribing of injustice
Bibliographic record
Abstract
National Sport Organisations in Canada have increasingly been incentivised to create their own equity, diversity, and inclusion (EDI) policies within the framework of national inclusive sport mandates. However, many people from equity-denied groups – including this article’s authors – continue to experience erasure, denial, and ignorance when engaging within Canada’s sporting system, not despite such policies, but sometimes because of them. Our Re-creation Collective of passionate practitioners and scholars from various equity-denied groups analysed all (143) Canadian national-level EDI sport policies available online. From this analysis, we created a model that explains common ways that EDI policies can serve to reproduce the very exclusions they seek to address. Our first theme, Reproducing the Status Quo, includes subthemes Alleging Inclusivity, and Refusing Accountability. In our second theme, Reproducing the Excludable Other, we discuss the subthemes Erasing, Problematising, and Hedging. We end with a critical discussion and knowledge mobilisation links aimed towards building better EDI policies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.027 | 0.034 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".