Selected Issues of (Good) Governance in North American Professional Sports Leagues
Bibliographic record
Abstract
In recent years, sport governing bodies (SGB) have been the subject of serious questions regarding their governance structures and decision-making processes. SGB that fail to implement regulatory mechanisms and to improve their governance structures and processes risk being confronted with severe ethically sensitive issues outside and inside the fields, which may eventually result in negative publicity and reduced demand (e.g., fans, sponsors) or financial support (e.g., from governments). This study examines selected regulations and practices of North American professional sports leagues in light of good governance principles. By adopting a qualitative research design, we investigate if there is a need for reforms to be employed by the leagues to comply with core dimensions of governance and thus reduce the risk of not being prepared to deal with ethically sensitive issues that may come up. Our critical analysis suggests that essential reforms need to be employed by the leagues to comply with core principles of good governance. In terms of democracy, professional leagues need to recognise stakeholder interests, implement innovative participation mechanisms, and apply diversity and inclusion policies for board composition. On transparency, it is required to publish internal regulations and financial information despite lax regulations on disclosure policies in the United States. Concerning accountability, professional leagues should separate their disciplinary and executive branches to avoid the concentration of power and potential conflict of interest in the relationship between the commissioner and team owners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".