Perpetuating the ‘lack of evidence’ discourse in sport for development: Privileged voices, unheard stories and subjugated knowledge
Bibliographic record
Abstract
Through an examination of the power relations embedded in the international movement of sport for development, we consider the dominant ‘lack of evidence’ discourse, which calls for more rigorous, scientific proof to validate the sport for development field. We argue that the lack of co-creation of knowledges, the politics of partnerships, and donor-driven priorities have subjugated sport for development practitioners’ knowledge, and therefore fueled this lack of evidence discourse. Acknowledging and privileging the contributions that typically female, young, black African sport for development grassroots practitioners’ knowledges make to the field will concomitantly result in a more robust evidence base and challenge the lack of evidence discourse.
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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 | 0.209 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.018 | 0.134 |
| Scholarly communication | 0.035 | 0.038 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.016 | 0.020 |
| 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".