Breaking into Bicycle Theft: Insights from Montreal, Canada
Bibliographic record
Abstract
Many cities have adopted policies that promote walking and cycling because of their positive environmental, economic, and social benefits. As bicycles become a more popular form of transportation and more bicycles are out on the road, planners and transportation researchers will have to consider not only how to create urban spaces that encourage cycling, but also how to discourage bicycle theft. Currently, bicycle theft often goes unnoticed and is largely unchallenged. The present research brings attention to this issue by providing a narrative on bicycle theft in Montreal, Quebec, Canada. A bilingual online bicycle theft survey was designed for this purpose and answered by 2,039 Greater Montreal residents. Summary statistics address ‘who’, ‘what’, ‘where’, ‘how’, and ‘when’ questions, a logit model determines variables associated with theft, and thematic maps compare experienced and expected theft between sub- regions. Half of respondents have had at least one bicycle stolen. Cyclists most frequently had their bicycles stolen in the downtown area. While, bicycles locked with U-locks, expensive bicycles, and those owned by women, are less likely to have been stolen. Satisfaction with bicycle parking availability and security tends to be low, and many cyclists are willing to pay for improved secure bicycle parking. Findings from this study can not only be useful to better understand and ultimately decrease bicycle theft in Montreal, but can also be beneficial for cyclists, police, and policy makers in other cities aiming to decrease bicycle theft as it highlights new findings in this unexplored area of research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".