If We Clear Them, Will They Come? Study to Identify Determinants of Winter Bicycling in Two Cold Canadian Cities
Bibliographic record
Abstract
This paper investigates the determinants of winter cycling in cold North American cities. For this purpose, two online surveys were implemented in the City of Ottawa and Montreal Canada during the winter season 2012. The main outcome of interest is whether the respondent cycles during winter. The variables collected in the survey included socio-demographics (age, gender, occupation, income, etc), as well as perceptions of winter maintenance and weather conditions with regards to willingness to cycle. Using a logistic regression method, the effects of adverse weather conditions, snow/ice on ground and winter maintenance are determined after controlling for socio-demographic factors and trip distance in both cities. From the results in the two cities, it is clear that the improvement of winter maintenance operations on bicycle infrastructure has a positive and significant impact on winter cycling. After controlling for other factors, increased maintenance of bike facilities in the winter months is expected to increase in 20% to 30% the propensity to bike in the winter in the study cities. Operations to produce facilities clear of ice and snow seems to be effective to encourage winter cycling rates in Ottawa and Montreal. Biking on facilities that are not completely free of ice and snow has a negative effect of 20% to 40% (Ottawa) and 30% to 50% (Montreal) in the likelihood to bike in winter. The probability of cycling during the winter is also associated to age, gender, cycling habits and adverse weather conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".