Building inclusive cities and mobility systems for immigrants in Atlantic Canada
Bibliographic record
Abstract
Traditionally, immigrant transit research in Canada has focused on the three largest census metropolitan areas (CMAs): Toronto, Montreal, and Vancouver (TMV). However, with the increase in immigration to small and mid-sized cities (SMCs), as seen in Atlantic Canada, the question of whether the transit systems in these SMCs will support the growing immigrant population remains. This question is particularly important as studies in large CMAs have shown that immigrants rely on public transit more than their Canadian-born counterparts. This thesis bridges the gap that exists in literature by examining immigrant transit experiences and policy responses in two mid-sized cities in Atlantic Canada: Halifax, Nova Scotia, and St. John’s Newfoundland and Labrador. Combining policy analysis, 12 key-informant interviews, 267 online surveys, and 20 ride-along interviews, this research identifies the barriers immigrants face when interacting with the transit space in these Canadian SMCs and the policy responses from the municipalities. The findings underscore that immigrants have varied, unique experiences that impact how they interact with the transit system in their destination cities. Some of these include navigating the unfamiliar landscape, navigating transit systems in winter, the stigma associated with transit use and car-centric culture, and mobility experiences across borders. More specifically, this project explores how to incorporate immigrant needs into transit planning, highlighting the importance of partnerships and engagement, fostering a culture of transit use, and reflecting community diversity in transit operations and workforce. Since the transit systems in Atlantic provinces have long struggled with adequate riders to justify investment in transit, recognizing immigrants as guaranteed riders will play a key role in the transit system being a tool for immigrant inclusion and retention.
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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 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.010 |
| 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".