Inevitable Tensions: Swiss and Canadian Sport for Development NGO Perspectives on Partnerships with High Performance Sport
Bibliographic record
Abstract
Over 400 sport for development non-governmental organizations (NGOs) have formed in recent years, operating projects in more than 125 countries globally. These NGOs typically focus on sport participation in countries in the Two-Thirds World, and usually have partnerships with their more established national sports organizations in their home country. Drawing on partnership theory, the purpose of this study was to analyse tensions underpinning partnerships with high performance sport from the perspectives of staff in Swiss and Canadian sport for development NGOs. Qualitative research methods were used, including a content analysis of the two NGO websites along with various organizational documents. Key staff from each NGO were also interviewed. The findings reveal three major tensions that both NGOs encounter. The first is competing values and this was tied to different approaches to sport programme delivery and concerns that NGO programmes are seen as a feeder system for their high performance sport partners. The second tension related to gaining legitimacy. While there were benefits in being associated with the established histories of high performance sport partners, the NGOs wanted to move the sport for development agenda forward independently but found it difficult to do so. Resource dependency was a third tension identified by both NGOs that shaped and were shaped by power imbalances between sport partners. The implications of the findings for sport for development NGOs and ideas for future research are discussed.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.058 | 0.026 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".