The power to shape policy: charting sport for development and peace policy discourses
Bibliographic record
Abstract
This paper discusses findings from a development policy discourse analysis that was conducted using six key sport for development and peace (SDP) policy documents. The research was guided by a theoretical framework combining postcolonial theory and actor-oriented sociology in order to critically analyse SDP policies. Based on this analysis, three theses are proposed: (1) SDP policies are unclear, circuitous and are underpinned by political rationalities; (2) coordinated and coherent SDP policy approaches between the One-Third World and Two-Thirds World suggest that ‘partnership’ is possibly akin to ‘developmental assimilation’; and (3) SDP policy models are wedded to the increasingly neoliberal character of international development interventions. Proposals for future research on SDP include an increase in the use of: (1) anthropological perspectives to uncover how those on the ‘receiving end’ of SDP policies are influenced and challenged by taking up the solutions and techniques prescribed for them; and (2) postcolonial perspectives that re-orient questions and concerns towards the Eurocentric standpoints couched in development policies, and asks scholars to uncover how power relations, authority and influence are embedded in the social processes of policy-making. The article concludes by arguing that SDP policies are messy, unpredictable, ambiguous and, at times, contradictory.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.018 | 0.070 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| 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".