Latent Bicycle Commuting Demand and Effects of Gender on Commuter Cycling and Accident Rates
Bibliographic record
Abstract
A recent survey by the City of Calgary, Canada, found that more than 75% of cyclists commuting to downtown Calgary are male. The intent of this research is to determine whether this is also true for cyclists commuting to a university campus located in the second most popular employment area of the city, what obstacles are preventing women from bicycling, and what measures could increase the number of female commuter cyclists. An online survey was conducted to collect information that allowed the grouping of respondents as potential, occasional, or regular cyclists. Analysis showed that women are more likely than men to be possible or occasional cyclists, while men are more likely than women to be regular cyclists. These findings suggest that if women's cycling needs were addressed, the modal share of bicycle commuting could be increased. Investigation of cycling barriers indicated that women are more concerned than men about safety issues associated with cycling, with being able to carry daily items while cycling, and with the need to fix their hair on arrival. In analysis of desired improvements, women were found to place a higher value on bicycle maps and literature but share similar facility preferences with men. High proportions of both genders indicated a desire for bicycle lanes, more pathways, and more direct bicycle routes. Analysis of falls and collisions suggested that men and women experience a similar number of falls per unit of exposure, while men experience more collisions per unit of exposure than women do.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".