Road Rage Experience and Behavior: Vehicle, Exposure, and Driver Factors
Bibliographic record
Abstract
Road rage has generated increasing public concern. Research has shown that victimization and perpetration of road rage is more common among males and younger drivers. We aimed to extend the understanding of determinants of road rage to driving exposure and vehicle factors, based on a 20022003 population survey of 1,631 regular drivers in Ontario, Canada. Regression analyses revealed that number of times drivers reported experiencing road rage in the previous 12 months was significantly greater for males, younger respondents, and those residing in Toronto. Also, victimization was significantly greater for drivers who did all their driving on busy roads and increased with number of kilometers driven on a typical week; however, type of vehicle driven was not significant. Number of times road rage perpetration was reported in the past 12 months was significantly greater for males, younger respondents, and those residing in Toronto, and lower for those in the Eastern and Northern region. Road rage perpetration increased significantly with number of weekly kilometers driven and was significantly greater for drivers who are always on busy roads and lower for those who never drive on busy roads, and higher for high-performance vehicle drivers. Even after controlling for driving exposure, road rage victimization and perpetration were highest for drivers in Toronto, where the pace of life may be more demanding. As expected, high-performance vehicle drivers reported more road rage perpetration. These individuals may experience more frustration when they are prevented from using the full performance capacities of their vehicles by crowded urban roadways.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".