Exploring gender differences in awareness of new active transportation projects: Réseau Express Vélo (REV) case study
Bibliographic record
Abstract
Many cities are currently planning and building new large-scale active transport networks to achieve a wide array of environmental, economic, and social equity goals. This is often combined with developing public campaigns to increase public awareness of their plans and introduced infrastructure to build a culture that celebrates and supports using them. While several studies explored the impacts of active transport infrastructure on users perception and travel behaviour, very little has been done to explore the factors associated with people’s awareness of such infrastructure at an early stage of their introduction. Accordingly, this study examines the factors affecting people’s likelihood of being aware of a new flagship cycling network in Montréal known as Réseau Express Vélo (REV), while understanding equity of awareness across gender identities. To achieve this goal, the study uses summary statistics and weighted multilevel logistic regressions to analyze data collected from a large-scale survey. The results of the paper show that various socioeconomic factors including age, ethnicity, income, language, as well as individuals’ travel behaviour and lifestyle are associated with being familiar with such a large bike network. Significant differences between women and men can also be observed, in which women are less likely to be aware of REV. Younger women in their 20 s tend to know much less about the project in comparison with men in the same age group. Women who identify as non-white only and used English to complete the survey are the group with the lowest probability of being aware of the project. Findings from this research unmask key aspects related to the likelihood of being aware of a new large-scale cycling network, offering important insights to transport planners, policy makers, and researchers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.004 | 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".