Determination and Quantification of Foreign Interest in Sports Using Selected Variables for the Support of Appraising Investments in Sports by Businesses and States
Bibliographic record
Abstract
The evaluation of the effectiveness of invested funds requires relevant data. This currently applies to investments in all areas of economic activity, including sports. The article’s aim is to determine the factors applicable to the quantification of interest in sports in selected countries. From the perspective of state funding of sports, foreign interest in sports is a part of the mechanism of allocating resources (situated in the Slovak Republic). For businesses, sponsoring sports organizations is a part of their activities connected to the concept of corporate social responsibility and environmental, social, and governance-related indicators. In both cases, it is important to have the necessary background data so that the entities responsible can correctly evaluate the effectiveness and return on such investments. The Czech Republic, Germany, Poland, and Hungary were selected to determine foreign interest in sports. These variables were selected for data analysis to quantify foreign interest in sports: competitions and tournaments, registered athletes, and keyword searches in Google trends. The variables predetermined the methods of data collection and statistical analysis. The main results lead to more accurate data for decision-making on investments in sports. The most popular sports in the given countries based on the interest quantification were identified.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".