The prevalence of road rage: Estimates from Ontario.Canadian
Bibliographic record
Abstract
Background: “Road rage ” has increasingly generated public concern, however, the prevalence of this behaviour has not been available. We examine the prevalence and demographic correlates of road rage victimization and perpetration based on a population survey of adults in Ontario. Methods: Data are based on the CAMH Monitor, a repeated cross-sectional telephone survey of Ontario adults (n=1,395). The contribution of demographic factors to road rage was examined with logistic regression analysis. Results: About half of respondents (46.6%) were shouted at, cursed at or had rude gestures directed at them in the past year, and 7.2 % were threatened with damage to their vehicle or personal injury. Nearly a third of respondents (31.7%) admitted to shouting, cursing, etc. at someone, and 2.1 % threatened to hurt someone or damage their vehicle. Being a Toronto resident, being younger, and earning a higher income were associated with greater likelihood of being a victim of shouting, cursing and rude gestures; however, income was not associated with being threatened with vehicle damage or injury. The
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".