Active Transportation in Kingston, Ontario: An Analysis of Mode, Destination, Duration, and Season among Walkers and Cyclists
Bibliographic record
Abstract
BACKGROUND: Individuals that engage in active transportation (AT) have healthier weights and fitness levels. Most AT research has focused on work- or school-based destinations. Meanwhile, little is known about the differences between individuals that engage in the most common forms of AT--walking and cycling--and how these AT patterns vary by destination, duration, and season. METHODS: We recruited 1400 randomly sampled adults (350 per season) in Kingston, Ontario, Canada to complete a cross-sectional telephone survey. The survey captured the prevalence, destinations, and duration of AT, and we examined the observed differences by mode. RESULTS: The majority (72%) of respondents were AT-users; walking constituted 93% of overall mode share. Cyclists were more likely to be male, younger, and employed than walkers. Walkers tended to access neighborhood-based destinations, while cyclists were more likely to use AT to get to work. AT duration was comparable by mode, ranging from approximately 8 to 20 minutes. Overall rates of AT were lowest in the winter, but walking rates were reasonably high year-round. CONCLUSIONS: Beyond commuting to work and school, policy-makers and planners should consider the breadth of destinations accessed by different modes when aiming to increase physical activity through AT in their communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".