VOLUNTARY RISK TAKING BY YOUNG BICYCLISTS: A CASE STUDY OF UNIVERSITY STUDENTS AT MONTREAL
Bibliographic record
Abstract
Young bicyclists in Montreal are taking voluntary risks such as bicycling without wearing helmet and braking system of bicycle although they are aware of the presence of danger. This article analyses the behaviour of young bicyclists taking voluntary risks. The university students in Montreal are considered as the case study since they are more risk takers and the bicycle is a favorite mode of transport among them. This study reveals that half of the respondents did not use helmets. They were also spontaneously taking a risk by high speeding, violating signals, bicycling in mixed traffic, ignoring protective equipment after dark, and avoiding the bicycle designated roads. They were taking voluntary risks based on their attitude, subjective norm and perceived behavioural control. They did not perceive the severity of risks since they experienced prelevant and unremarkable minor injuries. The attractiveness of risk and accomplishing the risk activities encouraged the young bicyclists to take voluntary risks repeatedly. Increasing feelings of vulnerability among young bicyclists reduce voluntary risks taking attitude and physical and psychological sufferings of the victims of bicycle-related accidents. Findings of this study suggest that the City of Montreal as well as other cities consider the behaviour of bicyclists particularly the young people to avoid bicycle-related accidents along with other physical measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".