Understanding and responding to the transit needs of women in Canada
Bibliographic record
Abstract
ABSTRACT: The historical practices of transportation planning are known to be gender-neutral and as a result have marginalized the experiences of a large sub-section of the population, namely women. Identifying the motives behind women's travel behaviours works to inform equitable data collection methods, transportation planning, and public transit policy. Correspondingly, understanding how public transit services and policies (curated with gender-neutral data and transportation planning principles) impact women's travel can reveal barriers to public transit usage. An inductive literature review of Global North grey and academic documents regarding women's travel behaviour (mode choice, travel route, time of travel and distance) and needs was conducted. The synthesis reveals that gender roles which lead to disparities in caregiving, income, employment, and security result in women being more likely (as compared to men) to complete trip chains, mid-day or off-peak trips, and shorter distance trips. A systematic policy review of 18 public transit systems from Canada's eight largest Census Metropolitan Areas (CMAs) and a webinar discussing public transit policy with female industry leaders reveals that the majority of public transit systems assessed do not explicitly account for gender differences when drafting actionable policy, service standards and data collection methodology. The identification of opportunities for gender inclusivity informs future research and policies regarding women's travel. Applying a gender lens to the creation of service standards, the introduction of new public transit technologies, the collection of real-time data, the creation of customer satisfaction surveys, and the evaluation of business cases and planning processes can result in the equitable consideration of women's travel needs in public transit service and delivery.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".