Regional Leagues as a Model for Success: The Cost-Benefit Analysis of Resurrecting the Yugoslavian Football League
Bibliographic record
Abstract
The purpose of this article is to show the financial potential of the old Yugoslavian league. Besides the sporting motive and improving competition, this league has a considerable financial potential. Football clubs receive revenues, mainly through three main activities: match day revenues, broadcast revenues and commercial revenue. Competition and creating a larger market will lead to an increase in all three types of revenues. By improving the competition, this league will attract larger finances. We look at stadium capacities, infrastructure, similar projects and the financial potential of this football league to determine the growth potential. Currently the clubs inside this potential league are operating in a much smaller market. This will increase the number of potential customers up to 20 million people. Methods: In addition to theoretical research and application considerations, the author uses a meta-analysis to conduct a comparative study based on historical and statistical data from electronic and printed media. During the preparation of the paper sources from universities, national data banks, and newspaper articles and other available data and statistics were also used. 1. Regionalization in sports Regionalization in sports is not a new phenomenon. What was previously only a Canadian league became regional when the Boston bruins were admitted in the National Hockey League (NHL), in 1924. (The People
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".