The Influence of Context on Occupational Selection in Sport-for-Development
Bibliographic record
Abstract
Sport-for-development (SFD) is a growing phenomenon involving engagement in sport activities to achieve international development goals. Kicking AIDS Out is one sport for development initiative that raises HIV/AIDS awareness through sport. Despite sport-for-development’s global prevalence, there is a paucity of literature exploring how activities are selected for use in differing contexts. An occupational perspective can illuminate the selection of activities, sport or otherwise, in sport-for-development programming and the context in which they are implemented. The purpose of the study was to understand how context influences the selection of sport activities in Kicking AIDS Out programs. Thematic analysis was used to guide the secondary analysis of qualitative data gathered with Kicking AIDS Out leaders in Lusaka, Zambia and Port-of-Spain, Trinidad and Tobago. Findings include that leaders strive to balance their activity preferences with those activities seen as feasible and preferential within their physical, socio-historical, and cultural contexts, and that leader’s differing understandings of sport as a development tool influences their selection of activities. To enable a better fit of activities chosen for the particular context and accomplishment of international development goals, sport-for-development programmes might consider how leaders are trained to select such activities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".