Improving the Implementation of Sport Governance with an Analysis of Its Determinants: The Case of Sport National Governing Bodies in Switzerland
Bibliographic record
Abstract
This research investigates new empirical routes to improve the implementation of sport governance by sports organizations. It asks the question: what determinants influence the decision of sport national governing bodies in Switzerland to implement sport governance? It builds on implementation deficits highlighted by benchmark analysis, an exploratory literature review and a description of the non-profit sector. This paper invites a reflection on the challenges and difficulties related to sport governance standards. The method builds on an exploratory, inductive and qualitative research design. The data comprise interviews with 10 decision makers of national sport federations and the national umbrella federation of sport and Olympic committee analyzed by theme-based coding. The analysis highlights five meta-themes associated with determinants: (1) strategic priority, (2) decision makers’ knowledge on the concept of sport governance, (3) sport governance issues, (4) resources, and (5) personal attributes of decision makers. The results and findings indicate that improving the implementation of sport governance is a multidimensional issue that mainly involves organizational and individual elements, and calls for a tiered approach rather than a “one size-fits all” approach.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".