Who does light rail serve? Light-rail transit and gendered mobilities in Montreal
Bibliographic record
Abstract
Investment in light rail transit (LRT) has been one of the main strategies of large metropolitan areas in the last decade to tackle environmental, economic, and social issues. In Montreal, Canada, a CAD$6.9 billion LRT system is currently under construction and is expected to significantly impact mobility patterns across the metropolitan region. It is crucial to identify the ways in which the impacts of such large public investment vary across societal groups to assess whether the distribution of benefit is fair and equitable. Using data from an online survey and a binary logistic modelling approach, we investigate the ways in which intentions to use this new LRT system differ across gender identities. First, we found that women are less likely to have an intention to use LRT compared to men. Our modelling results show that there are statistically significant differences across gender identities in the effect of certain sociodemographic and travel-behavior characteristics that explain the intention to use the LRT system. In terms of trip purpose, whilst women and men intend to use LRT for work trips to the same extent, men intend to use LRT for leisure and discretionary travel more than women. Our findings can help in guiding further research into gender gaps in transport studies and inform practitioners on how gender can be considered in LRT policy decisions so that the benefits of major public-transport investments are more equitably distributed.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 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".