A Research into Evaluation of Basketball Athletes’ Risk Perception Level
Bibliographic record
Abstract
The aim of this study is to compare the risk perception levels of Basketball athletes in Turkish League teams according to some variables. In this research the “general screening model”, which is one of the descriptive screening methods, was used. While the population of the study consists of athletes actively engaged in the Turkish basketball league teams, its sampling consists of 229 athletes selected by chance and at random methods, which have been playing in different clubs. The questionnaire of Gok was used as a data tool in the study to perception of risk in sports. The difference between the risk perception levels according to the variables of gender, education level and years of playing basketball of the athletes who participated in the study were statistically significant. However, according to the variables of marital status and age of the athletes, the difference between the levels of risk perception statistically was not significant. Results showed that the average risk of female athletes were higher than the average risk of male athletes. The average risk of the athletes educated at high-school level were lower than the average risk of athletes educated at university and postgraduate level, average risk of athletes who play basketball between 1-5 years was lower than the average risk of athletes who play basketball between 6-10, 11-15, 16-20 and above 21 years. Basketball clubs, in order to be robust, should have professional managers in their club managements. Sports clubs should employ specialized advisors regarding the risks that players may encounter. In sports clubs, satisfaction of the players should be the first goal and expectations from them should be clearly specified. Sports clubs should also ask the players’ opinions when preparing strategic plans against the risk factors that may occur.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".