Examining Risk-Taking Behavior and Sensation Seeking Requirement in Extreme Athletes
Bibliographic record
Abstract
Extreme sports are sport branches which include actions, adventures, risks and difficulties more rather than other sports. Special materials are used in sport branches such as surfing, kite surfing, sailing, snowboarding, paragliding, diving, mountaineering, motor sports and adrenaline release is more rather than in other sport branches. On the contrary, the situation for being eager to seek excitement and take risks with a view to having new experiences has been observed. It has been considered whether sensation seeking requirement and risk-taking behavior had effects upon each other. The aim of the study was to analyze sensation seeking and risk-taking behavior in extreme athletes. Total 101 extreme athletes including 31 females, 70 males with an age average of 22.03 ± 6.77 participated in the research. In order to determine athletes’ sensation seeking levels, “Arnett Inventory of Sensation Seeking” developed by Arnett (1994) and in order to determine their risk-taking behavior, “Risk Involvement and Perception Scale” developed by Siegel et al. (1994) were used. In evaluation of research data, frequency analysis, independent t test, in determination of relation between risk-taking and sensation seeking, correlation test were utilized.In conclusion, significant differences were found in risk-taking behavior, sensation seeking requirement and gender variable among the extreme athletes. In the male athletes sensation seeking requirement and risk-taking behavior had higher averages than the female athletes. Among the extreme athletes, significant relations were determined between risk-taking behavior and sensation seeking requirement. When risk-taking behavior values were high, sensation seeking requirement values were regarded to be high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".