A systematic review of governance principles in sport
Bibliographic record
Abstract
Research question Given the plethora of governance principles proposed by academics, government agencies, and sport governing bodies, this study systematically reviewed the current landscape of governance principles in sport.Research methods Following the PRISMA, PIECES, and the University of Warwick protocols, a search of academic and grey literatures resulted in 594 unique records. After screening the records for relevance and quality, 73 records (12%) remained.Results and findings Most sources were non-empirical, originating from academic working groups and sport governing bodies located predominantly in Europe. Overall, 258 unique governance principles were found. Transparency, accountability, and democracy dominated frequency-wise, while Board-related principles were the most popular focus, followed by stakeholder engagement. The list of principles was synthesized through an inductive thematic analysis into four categories: structure-based, process-based, outcome-based, and context-based. Empirical studies demonstrated governance principles’ assessments in national and international sport organizations to be average at best.Implications Findings highlight the systemic and multi-dimensional nature of governance. The four governance principles categories point to academics and practitioners seeing/enacting governance in different ways: structurally at different levels of the organization (i.e. including and beyond the Board), in the organization’s managerial processes, as desired organizational outcomes, and according to their specific context. Researchers and practitioners should endeavour to be purposeful in their use of terms (e.g. ‘principle’ vs ‘indicator’), define their terms, and offer greater details to present higher quality assessment outcomes. We encourage researchers to use more robust, evidence-based governance principles and sophisticated measures/advanced analyses in future assessments of governance.
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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 | 0.037 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".