High-Risk Driving Attitudes and Everyday Driving Violations of Car and Racing Enthusiasts in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Attitudes and individual difference variables of car and racing enthusiasts regarding high-risk behaviors of street racing and stunt driving have recently been investigated. Positive attitudes toward high-risk driving, personality variables such as driver thrill seeking, and other self-reported risky driving acts were associated with these behaviors. However, probable relationships among high-risk driving tendencies, everyday driving behaviors, and negative road safety outcomes have remained largely unexamined. This study aimed to investigate the associations among car and racing enthusiasts' high-risk driving attitudes, self-reported everyday driving violations (i.e., ordinary and aggressive violations), and self-reported negative outcomes (i.e., collisions and driving offense citations). METHOD: A web-based survey was conducted with members and visitors of car club and racing websites in Ontario, Canada. Data were obtained from 366 participants. The questionnaire included 4 attitude measures-(1) attitudes toward new penalties for Ontario's Street Racers, Stunt and Aggressive Drivers Legislation; (2) attitudes toward new offenses of stunt driving under the same legislation; (3) general attitudes toward street racing and stunt driving; (4) comparison of street racing with other risky driving behaviors-self-reported driving violations (i.e., ordinary and aggressive violations); self-reported collisions and offense citations; and background and driving questions (e.g., age, driving frequency). RESULTS: Results revealed that attitudes toward stunt driving offenses negatively and general attitudes toward street racing and stunt driving positively predicted ordinary violations, which, in turn, predicted offense citations. Moreover, general attitudes toward street racing and stunt driving positively predicted aggressive violations, which, in turn, predicted offense citations. CONCLUSION: The findings indicate that positive high-risk driving attitudes may be transferring to driving violations in everyday traffic, which mediates driving offense citations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".