Rules of the Road: Compliance and Defiance among the Different Types of Cyclists
Bibliographic record
Abstract
Although cycling has become a more attractive option to commuters in many North American cities recently, significant apprehension remains around its safety. Though risks experienced by cyclists are diverse, the idea that they are because of scofflaw cyclists—cyclists who regularly ignore the rules of the road—remains prevalent. Improving cycling safety requires countering this idea, and therefore an understanding of how different cyclists act under the existing rules. Using a survey of 1,329 cyclists in Montreal, Canada, this study generated a typology of cyclists based on cycling motivations and behaviors and conducted comparisons based on their responses to four cycling rule-breaking scenarios. Our study shows that all cyclist types contravene traffic laws in similar ways, and 0.6% of respondents consistently follow the traffic laws. Breaking the law was often considered the safest option by respondents, which reflects a disconnect between the safety goals of traffic laws and the reality on the streets based on the perspectives of different cyclist types. Although cyclist types may act similarly in response to existing laws, they still respond uniquely to policies aimed at increasing rule adherence. Targeted interventions aimed at educating young cyclists, improving dedicated infrastructure, and prioritizing cycling traffic could increase rule compliance across all types. Through our study, planners, policy makers, and law enforcement could improve cycling safety by better understanding the behavior and rationale taken by cyclists.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".