Marginalized Communities and Transit Development: A Study of Light Rail Transit Lines in Toronto, Canada and Area
Bibliographic record
Abstract
The Province of Ontario, Canada is preparing to update their transportation infrastructure to accommodate a projected increase in the province's population. Connecting people, reducing congestion and emissions in the Toronto area is the main focus of these transit development projects. It is understood, however, that upgrading infrastructure is a vital undertaking for every community. However, it seems as though decision makers don't always consider the effects these projects have on racialized and/or low-income people in their communities. When making plans, are enquiries made to see if benefits from these networks are evenly distributed and experienced? Studies examining neighbourhood change are numerous. Indeed, the effects, both negative and positive, and their outcomes are widely studied. However, this study considers neighbourhoods in the Greater Toronto Area (GTA) where transit development will have an influence on particular communities. Additionally, this study's primary focus is Light Rail Transit (LRT). It looks at marginalization in GTA communities (as defined in the Ontario Marginalization Index) in relation to light rail transit development in construction, or planned, to see if they are more likely to be impacted at a regional level. The spatial patterns of these populations are explored through Global and Local Moran's I. Although observations are made on a regional scale, the local case of Cooksville, Mississauga is also explored to provide context.
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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 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".