Support for Active Transport Policy Initiatives Among Canadian Adults: The Canadian National Active Transportation Survey
Bibliographic record
Abstract
Objectives: To examine public support for active transportation (AT) policies and to identify demographic and behavioural predictors of support for each policy approach.Methods: Canadian adults aged 18 years and older (N = 2,868) provided information on demographic factors (e.g., age, income), place of residence (e.g., region, size of community), and the frequency with which they walked/wheeled or cycled to work or school in a typical week. In addition, they rated their support for AT-related policies (e.g., spending government money on more dedicated bicycle paths, offering tax credits for public transit passes, charging higher rates for parking to subsidize costs for AT infrastructure, changing the design of neighbourhoods and communities to encourage informal physical activity). Multinomial regressions examined demographics and AT behaviour as predictors of support for each policy approach, yielding a total of eight models.Results: Although most policy actions to promote AT were supported by Canadians, the level of support varied by the type of policy actions and by demographics and AT behaviour. A majority of Canadians supported policy approaches targeting environmental planning and fiscal measures that incentivized AT. A minority of Canadians supported policies aimed at regulation or coercive fiscal measures. The level of support for AT policies was higher among women, those with more education, younger respondents, those residing in central and eastern Canada, and individuals who engaged in AT.Conclusion: Canadians are supportive of policy actions to facilitate AT. This public support might be important for their future development and implementation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".