Exploring Congruence in Global Sport Governance between Environmental Policy and Practice
Bibliographic record
Abstract
Global sport governance and environmental sustainability is a novel topic in the academic literature. This paper explores global sport governance with respect to the congruence exhibited between website disclosures of environmental policy and the implementation in practice by the Association of Summer Olympic International Federations (ASOIF). Congruence is noted as a requirement for success. To begin, this paper discusses the notions of global sport governance, environmental policy and governance, and congruence for environmental sustainability. Next, content analysis is used to explore the various environmental disclosures and initiatives by the ASOIF. The results provide evidence of the environmental governance conducted by the sport organizations. A total of 44% of the organizations under study did not report any environmental disclosures or initiatives; however, the majority supported the growth of environmental action by sport with disclosures on their websites. The results outline the current achievements with respect to congruence through three key elements, including formulated strategies, policy development, and implementation, which are noted as leading to successful environmental sustainability. This study offers a baseline concerning the status of these international sport organizations and the pursuit of environmental sustainability.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".