On your left! Why Montreal sees more cycle commuting than Toronto
Bibliographic record
Abstract
Toronto sees significantly lower rates of bicycle commuting than does Montreal, a city with nearly 1 million fewer people.Existing research on cycling rates largely points to the influence of different built environments and policy decisions to account for this disparity, with little information available as to why these differences exist.Drawing on scholarly research, popular media, as well as government and historical documents, I examine how Toronto and Montreal's cycling landscapes came to be.My research shows that an influential grassroots cycling advocacy ecosystem existed in the early years of the bicycle's popularity in Montreal, where no comparable network existed in Toronto.These findings illustrate the importance of a strong popular cycling movement for the construction of cycling infrastructure and the implementation of relevant cycling policies. RésuméToronto compte beaucoup moins de cyclistes que Montréal, ville qui compte près d'un million d'habitants de moins.Les recherches existantes sur les taux de pratique du vélo soulignent largement l'influence des différents environnements bâtis et des décisions politiques pour expliquer cette disparité, mais peu d'informations sont disponibles sur les raisons de ces différences.En m'appuyant sur des recherches universitaires, des médias populaires, ainsi que des documents gouvernementaux et historiques, j'examine comment le paysage montréalais, plus favorable au vélo, s'est développé.Mes recherches montrent qu'un écosystème local influent de défense du vélo existait dès les
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.002 |
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".