The Evaluation of the Degree of Applying Risk Management Behaviors among Sports Councils in East of Iran
Bibliographic record
Abstract
The aim of this study is to determine the degree of applying risk management behaviors among managers and directors of sports councils in Khorasan province which is located in the east of Iran. The method was descriptive-analytic, and conducted on all the managers and directors of sports councils. The samples was included 126 managers and directors of sports councils in Khorasan who had been working in the East area (Khorasan). The questionnaire (risk management, Aeron. 2004) was used and Cronbach Alpha was .87 which was satisfactory. Descriptive and inferential statistics (Kruskal Wallis H, Kendall & Pearson), have been used in data analysis. The findings of the research showed that there isn’t any meaningful relationship between levels of education, the employment status, coaching status and risk management behaviors, while there was meaningful relationship between field of education)p? 0.006), coaching experience(p?0.033, r=0.185), managing experience(p?0.001, r= 0.260) and risk management behaviors. As a whole we can say that the level of performance of risk management behaviors among the sports councils in Kuhorsan province has a desirable condition. It was also found that staff directors performed risk management behaviors better than line directors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".