Mahle International GmbH, Germany
Bibliographic record
Abstract
Motor racing includes high speed driving and risky maneuvers and can result in negative outcomes for both spectators and drivers. Interest in motorsports is also associated with risky driving attitudes and behaviors on public roads as well as with individual difference variables, such as sensation seeking. However, whether the links between motorsports involvement and risky driving tendencies differ for spectators and drivers has remained mainly unexamined. The aim of this study was to investigate the relationships between thrill seeking, attitudes toward speeding, and self-reported driving violations among a sample of motorsports spectators and drivers.A web-based survey was conducted and sampled 408 members and visitors of car club and racing websites in Ontario, Canada. The questionnaire included measures of (i) motorsports involvement, (ii) thrill seeking (Driver Thrill Seeking Scale), (iii) attitudes (Attitudes toward Speed Limits on Roadways and Competitive Attitudes toward Driving Scale); (iv) self-reported driving violations (adapted from Driver Behaviour Questionnaire), and (v) background variables. Path analysis was performed to test the relationships among the variables.For both spectators and drivers, thrill seeking directly predicted driving violations; competitive attitudes toward driving further mediated this relationship. Attitudes toward speed limits, however, mediated the relationship between thrill seeking and violations only for drivers.We observed significant relationships among individual difference measures, motorsports involvement, speeding attitudes and violations that may inform road safety interventions, including differences in the relationships among thrill seeking, speeding attitudes, and violations for motorsports spectators and drivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.422 | 0.418 |
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".