MOTORISTS' PERCEPTIONS OF AND RESPONSES TO WEATHER HAZARDS. IN: WEATHER AND TRANSPORTATION IN CANADA
Bibliographic record
Abstract
This study explores drivers' perceptions of and responses to weather hazards experienced in Southern Ontario, and compares these to objective estimates of risk. These findings are discussed in terms of their implication for driver education regarding weather-related hazards. Group interviews were first conducted with a small sample of Ontario drivers, focusing on hazard perceptions. A large sample public survey was then conducted which focused particularly on weather-related driving hazards. A parallel study was then carried out with driving instructors. Findings suggest that respondents understand the seriousness of weather-related hazards. Drivers' perceptions of the relative seriousness of weather-related hazards generally correspond well with the estimates of relative risk provided by empirical accidents. However, the majority of drivers make only minor adjustments to the perceived hazards, primarily by driving more slowly. The survey of driving instructors revealed that driving instructors have similarly accurate perceptions of the risk from weather hazards, but driver education programs provide little information or practical experience to new drivers about how to cope with different weather conditions. These results suggest that there is little need to educate the public about the inherent risk from bad weather conditions. However, driver education programs can do more to teach appropriate avoidance strategies in different weather and driving conditions, and to encourage drivers to make reasonable tradeoffs between mobility and safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".