Revisiting Sport-for-Development Through Rights, Capabilities, and Global Citizenship
Bibliographic record
Abstract
Sport-for-development (SfD) refers to the use of sport to meet non-sport goals, such as health promotion, gender empowerment, social inclusion, and peace building and conflict resolution. SfD research is often concerned with the impacts, outputs and overall efficacy of SfD activity and programming, and particularly the question of whether and/or how sport (broadly defined) may contribute positively to social development, and on an international scale. At the same time, critical SfD research has also emerged. In particular, a specific critical insight to emerge from this literature is that the current conceptualization, mobilization and implementation of SfD aligns more with processes of social reproduction than with the pursuit of social change. With this critique in mind, in this chapter we explore ways in which SfD might be (re)conceptualized as a commitment to social change more so than social reproduction. We do so by first revisiting the notion (and importance) of human rights as a foundation for SfD, in both theory and practice. We then use this (re)commitment to human rights as a departure point from which to discuss two possible conceptual models of and for sport-for-development—capabilities and Global Citizenship—that we posit may support a (re)committed approach to rights and justice, and in so doing hold more potential to generate social transformation than reproduction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".