Bridging the gap: A social equity analysis of intra-city transit access to inter-city rail in Canada
Bibliographic record
Abstract
To support the decarbonization of inter-city transport and reduce the risk of marginalized populations being excluded from long-distance travel, intra-city transit systems should provide equitable access to inter-city rail network nodes. This study evaluates transit-based accessibility to inter-city rail stations in seven Canadian cities along the Québec City–Windsor corridor through an equity lens. Specifically, we evaluate accessibility inequalities using Gini coefficients and three accessibility ratios based on income, race, and age. While Gini coefficients reveal no clear link between city size and inequality, the three socioeconomic status-based accessibility ratios indicate that marginalized population groups face significant inequalities in the two largest cities: Toronto and Montréal. In these two cities, low-income individuals, visible minorities, and older citizens experience inequitable accessibility, with longer transit travel times to inter-city rail stations. These findings highlight the uneven distribution of transit access to inter-city rail services in larger cities, potentially deepening social exclusion for vulnerable population groups and hindering the transition to sustainable inter-city travel. By examining intra-city transit access to inter-city rail through a social equity lens, this study offers valuable insights into the social equity of intermodal connectivity in Canada and provides a framework for similar assessments in other geographic contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".