Assessing Geographic Context in Relation to Public Transit Experience in Toronto
Bibliographic record
Abstract
This research examines how geographic context affects residents’ experiences with public transit in Toronto, with a focus on equity, accessibility, and social sustainability. Using the theoretical lens of the Right to the City, this study investigates the lived experiences of transit users across three distinct sites: Bloor-Yonge Station in the downtown Toronto core, York University Station in North York, and Kennedy Station in Scarborough. These locations represent diverse socio-economic and demographic contexts within the city. Using a qualitative methodology, this research combines participant observation with open-ended questionnaires to explore how service reliability, accessibility, safety, and first- and last-mile connections vary across neighbourhoods and influence transit use. Findings revealed systemic inequities in the quality, reliability, and convenience of transit service, disproportionately affecting marginalized groups such as low-income, racialized, and disabled riders, particularly in suburban areas. While downtown riders face overcrowding and wayfinding challenges, users in North York and Scarborough experience longer travel times, infrequent service, and inadequate infrastructure. This study emphasizes the importance of transit planning that considers geographic context and the diverse needs of users to promote equitable transit usage and social sustainability. Insights from this research can inform more inclusive and effective transit policies that better serve the needs of each community in Toronto.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".