PROFESIONALNI SPORT U REPUBLICI HRVATSKOJ ZA VRIJEME COVID-19 KRIZE: FROM A THREAT TO AN OPPORTUNITY FOR A BETTER STATUS OF PROFESSIONAL ATHLETES
Bibliographic record
Abstract
The paper aims at analysing Croatian professional sport and the impact of the COVID-19 crisis on it. Football was taken as a model for other team sports because of the share of professional sports clubs in the Republic of Croatia in football. In addition, the legal framework set in football may apply to other sports for successfully developing a similar pattern. The analyses are conducted into the revenues (sponsorships, ticketing and TV rights), and expenses (expenses on behalf of players remuneration) of the football clubs in the First Croatian Football League, providing an overview of the professional status of sports clubs, athletes and coaches. It also encompasses an analysis into measures taken by the Government of the Republic of Croatia to support Croatian sport during the COVID-19 crisis. The COVID-19 crisis has been taken as a possible starting position for better regulation of sports in future, especially as regards the professional sports in the Republic of Croatia contributing significantly to the promotion of the Republic of Croatia worldwide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".