Sport-for-Development: A Level Playing Field?
Bibliographic record
Abstract
In the burgeoning field of sport-for-development, the benefits of participation for youths have been widely discussed. However, it has also been noted that some youth are excluded based on ability, location, economic means, and gender and are thus not participating. We considered that this might be an issue of ideologies. Thus, it was the purpose of this study to use a critical occupational approach to explore how sport-for-development ideologies in Zambia shape the participation of young people. Drawing on empirical data gathered from five case studies of sport-for-development organizations in Lusaka, Zambia, three themes were identified that describe ideological beliefs within the Zambian sport-for-development context. The first, sport benefits all, contributed to the practice of sport being used uncritically as an activity for all youth. The second, good people do, perpetuated what were considered acceptable activities that boys and girls could do in the local context. Finally, a belief that sport is the way out privileged boys who play football as well as athletic non-disabled boys in opposition to girls, poor youths, rural youths, and girls and boys with disabilities. Together these beliefs have contributed to successes (careers in sport) and shortcomings (occupational injustices) associated with the sport-for-development phenomenon. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1502120
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".