Systematic Reviews of Sport for Development Literature: Managerial and Policy Implications
Bibliographic record
Abstract
This paper reports findings of two systematic reviews of Sport for Development (SfD) evidence, with a particular focus on managerial and policy implications. We suggest that the outcomes of the systematic reviews yield significant insights regarding the current state of the SfD literature, particularly with respect to the diversity of interventions, the importance of scale and context, and the general paucity of rigorous, empirical analyses. In turn, we propose that several managerial and policy implications and recommendations can be gleaned from these assessments, including the importance of an ongoing and even renewed commitment to theory and context, and critical considerations of the structure of the SfD field itself. We also use the results of the reviews to make suggestions about the importance of future research in this area, as well as the kind of research that is needed, in both policy and programming terms.
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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 | 0.273 | 0.600 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".