Unsafe driving behavior and safety law support: unraveling the influence of drivers’ demographics
Bibliographic record
Abstract
Understanding how demographic attributes influence risky driving and support for traffic safety laws is essential for developing targeted regulations. This study analyzes self-reported data from Canadian drivers to identify high-risk groups using accident history and acceptance of unsafe behaviors. Two cases are examined: Case 1 focuses on accidents and demerit points; Case 2 focuses on acceptance of risky behaviors. The analysis involves k-means clustering to classify risk groups, factor analysis to group safety regulations into three categories—speeding, distracted/intoxicated driving, and red-light violations—and logistic regression to explore demographic associations. Key findings show that driving experience, income, and region influence risk in Case 1, while vehicle size, driving frequency, gender, age, and income are significant in Case 2. Senior drivers tend to support stricter safety laws opposing distracted and intoxicated driving, over-speeding, and red-light violation. The study’s results can inform targeted driver education, address high-risk groups, and enhance traffic regulation policies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".