My long distance low commitment casual bicycle: Understanding BIXIs first winter
Bibliographic record
Abstract
Encouraging cycling year-round has been acknowledged as both a need and a challenge in North American winter cities like Montreal, QC. While the wide-ranging benefits of cycling for transportation are well-documented, winter conditions often deter riders. In 2023, Montreal’s bike-share company BIXI introduced a pilot year-round service, extending their usual AprilNovember season through the winter. Given bike-share's unique ability to attract new cyclists, BIXI’s 2023-24 winter service represents a key development in the advancement of winter cycling. In this thesis, I use data from an online questionnaire survey as well as open data from BIXI and EcoCounter to understand the outcomes of BIXI’s pilot project, both for the bike-share service and for winter cycling in Montreal more broadly. My analysis confirms established trends in winter cycling literature on demographic disparities and the role of convenience and perceived safety in decisions to cycle. Yet, I also present a unique user-focused perspective on the “enjoyment” factor of winter cycling, highlighting its significance for future behaviour retention. I identify moments of success and areas for improvement for BIXI, as well as suggestions for policymakers seeking to grow winter cycling in Montreal and in other winter cities. This research contributes new primary data to the limited literature on winter bike-share, supporting the development of more inclusive, resilient, and sustainable year-round active mobility systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".