Supportive cycling environments for women
Bibliographic record
Abstract
Cycling is recognized as an important mode of transportation. It is affordable, produces no greenhouse gases, provides a form of exercise, and requires infrastructure that is cheaper to build and maintain compared to personal vehicles. However, in low-cycling countries like Canada, women comprise a disproportionate share of total cyclists, between 20-30%, which has implications for equity, and the health of the population and environment. The academic literature offers few, and only theoretical, solutions to improve the gender disparity, all of which are theoretical. This practicum fills a portion of this gap by identifying real world interventions that assist women to cycle and asking how these could be implemented in Winnipeg. A precedent review returned ten interventions including women-specific cycling courses, events, rides, and mentorship programs. Key informant interviews with Winnipeg cycling advocates provided a better understanding of Winnipeg’s cycling environment, and how the interventions could be implemented in the city. Some were considered feasible to implement while other interventions were too context specific. Two barriers hindering efforts to adopt interventions supportive of women who cycle or wish to, are the continued emphasis on physical bicycle infrastructure and the belief that programming targeted to the general population is sufficient. The findings provide several opportunities for further research including: completing the precedent review in additional languages and conducting a focus group with municipal transportation planners and bicycle advocates to better understanding the barriers identified in this practicum and determine the next steps for reducing the gender disparity in Winnipeg.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".