Rethinking Sportland: A New Research Agenda for the Sport for Development and Peace Sector
Bibliographic record
Abstract
Sport for Development and Peace (SDP) has grown into a huge global field of sport-related activity and intervention and is a heavily researched subject in the social scientific study of sport. In this article, we advance the case for a new research agenda in SDP, in part to contribute more fully to sustainable development through substantial societal change. We argue that SDP research should engage with wider literatures and theories, notably on political economy and development; take full account of structural changes within the development sphere; and examine new areas of intervention within SDP per se. To develop our analysis, our discussion is organized into six main parts. We begin by introducing the concept of “Sportland” to reimagine SDP as a strongly institutionalized field of development activity with its own stakeholder networks. Second, we outline the key aspects of prior SDP/Sportland research on which we seek to build. Third, we examine key changes in the political economy and geopolitics of development, which serve to point Sportland scholars toward engaging with fresh literatures in these fields. Fourth, we explore the implications of these changes to retheorize development. Fifth, we detail new ways ahead for Sportland with regard to policy, practice, and research, with particular reference to the position of different organizational stakeholders within SDP. Finally, we consider specific areas of future intervention and inquiry within Sportland that require the attention of researchers. Our analysis is underpinned by many research studies and projects in Sportland which we have undertaken separately or collectively over at least the last decade.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".