Policy vs. practice in sport and climate change: the perspectives of key actors in global sport and international development
Bibliographic record
Abstract
Despite widespread, scientifically supported recognition of the scope of the climate crisis, and policies in place connecting sport to sustainable development, there remain concerns that the environment and climate change are rarely acknowledged within SDP activity and that even when they are, it is unclear how such policies are implemented, and to what effect. This raises the question of how and why the climate crisis and the attendant relationships between sport and sustainable development are understood and operationalized (or not) by stakeholders within the SDP sector. In this paper, therefore, we explore various perspectives and tensions around the environment and climate crisis within the SDP sector. To do so, we draw on interviews with SDP policy-makers (primarily from the United Nations and the International Olympic Committee) and SDP practitioners living and working in the global South in order to gauge the place of the environment and climate change in their everyday SDP policy-making, programming and practices. Overall, the data shows that while SDP stakeholders recognize the urgency of the climate crisis, the need for action, and the policy agenda linking sport to sustainable development, significant barriers, tensions and politics are still in place that prevent consistent climate action within SDP. Policy commitments and coherence are therefore needed in order to make climate action a core feature of SDP activity and practice.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| 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".