Bibliographic record
Abstract
In the broader business law literature, much has been written on the supposed convergence trend of corporate governance practices. Yet this academic discussion has barely extended to the professional sports context and in the instances where professional sports governance has been at issue, stadiums and stadium ownership have not been the subject of analysis. With stadium construction and renovation projects regularly running into the hundreds of millions or billions of dollars, and ongoing stadium operations and debt repayments on such facilities often exceeding tens of millions each year, stadium governance is a significant aspect of business and corporate governance worth illuminating. This article aims to contribute to the closing of this literature gap.Although there are many prospective paths of inquiry, this study focuses on stadium ownership structures in four wealthy Anglosphere jurisdictions with a substantial professional sports and stadium presence: England, the United States, Canada, and Australia. Beginning with the baseline of the English Premier League as a proxy for England, and continuing with a comparative examination primarily focused on the National Football League (NFL), Major League Soccer (MLS), Canadian Football League (CFL), Australian Football League (AFL), and A-League, this study evaluates 114 stadium ownership structures. After a literature review on corporate governance convergence trends, stadium finance, and motivations for stadium construction, I move to a descriptive overview of the stadium holding structure data set. This is followed by the core discussion of a number of legal influences on stadium ownership as well as the relationship of stadium ownership to club controlled ancillary real estate development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".