The Impact of Transit Development on Racialized Neighbourhoods in Toronto: A Case Study of Little Jamaica
Bibliographic record
Abstract
The linkage between transportation planning, gentrification, and anti-Black racism is not sufficiently addressed in contemporary debates about urban development in Toronto. To unpack this relationship and its encompassing effects, this Major Paper examines two urban policies driving urban change in Toronto’s Little Jamaica in the context of the Eglinton Crosstown Light Rail Transit (LRT): transit-oriented development and cultural-led revitalization triggering gentrification and displacement. The Eglinton Crosstown is, to date, the largest infrastructure project in Ontario. With a $5.3 billion dollar investment over 11 years, it is set to be completed in 2022 by the Provincial transit agency Metrolinx. In theory, the LRT will improve transportation accessibility to low-income communities who historically have not benefited from such investments. In practice, however, the construction of the LRT has exposed anti-Black racism and heightened local socio-economic vulnerabilities, revealing the gentrifying effects of the Eglinton Crosstown’s approach to transportation planning and urban policy, particularly, as it has forced the closure or displacement of local Black businesses and currently threatens Little Jamaica’s historical Black community. In response, The City of Toronto is in the process of validating the significance of the Eglinton corridor as a distinct cultural district for Caribbean and African immigrants; however, this raises concerns over the commodification of Little Jamaica’s Black heritage and culture. This Major Paper documents the irreversible changes that have occurred in Little Jamaica and concludes with policy recommendations to mitigate further gentrification and displacement in the area, and some reflections on lessons for equity planning and urban policy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| 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".