“Building Back Better”: Seeking an Equitable Return to Sport for Development in the Wake of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic affected sport programming by restricting in-person activities. Concurrently, global outcry for racial justice for Black and racialized communities promoted calls to action to assess equitable practices in sport, including sport for development (SFD). This study critically examined SFD “return to play” programming to include perspectives from racialized persons’ lived experiences. We present findings based on data collected from Maple Leaf Sports and Entertainment Foundation’s Change the Game campaign, which explored questions of sport inequity to “build back better.” Outcomes further SFD discourses challenging (potentially) harmful structures affecting participants, including underreported effects of racialization. The study used both quantitative and qualitative analyses of survey data on youth experiences, enablers, and barriers in sport and analyzed these results within an antiracist, antioppressive, and decolonial conceptual framework.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".