OLE OF SPORT INDUSTRY IN UNIVERSITY GRADUATES EMPLOYMENT
Bibliographic record
Abstract
Today; unemployment is considered as a sign of undevelopment of world countries. In themajority of countries; real unemployment in urban areas is about 15-25% of employments (1).One of the most important economic effects of sports industries is creating employmentopportunities. Sports industry made job opportunity for 60 to 80 million employees around theworld. American Sports employees include of 4.5 million and for the 15 members of EuropeanUnion is about 1.5 to 2 million that have been announced (7). In Iran; unfortunately; there is nocomprehensive information about the quantity of employees in sports part; but according toSeminar on Employment and Entrepreneurship (1382); 25000 employees has been estimated thatis around %1.07 of the country’s employers. However; in Hong Kong is 2.5; Canada andEngland 2; Australia and Scotland %1.9 and New Zealand %1.3 of the employers in thesecountries is employed Sports.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.004 |
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".