Toronto’s Golden Mile Urban Redevelopment Project: Intensifying Social Exclusions or Increasing Opportunity?
Bibliographic record
Abstract
The Golden Mile, a historically light industrial neighbourhood on Toronto’s eastern outskirts is poised to undergo a significant neighbourhood-level redevelopment. 15 private developers have successfully proposed a series of 76 new mid-rise buildings, expected to attract 45,000 new residents over the next 20 years. To date, private developer discourse and promotional material has marketed the redevelopment as an opportunity for neighbourhood-wide economic rejuvenation. However, existing community members, especially more vulnerable low-income residents with families are skeptical. Fears of rental appreciation, discrimination, the burden of construction, as well as the potential loss in community networks and clientele amongst local businesses and social sector agencies has amplified concern. Armed with this stakeholder insight, interventions can be made before it’s too late. By prioritizing community equity ownership and job participation in the development’s construction, as well as the thoughtful use of government policy, such as vacancy and affordable housing bylaws and tax incentives to incent affordable units, inclusive local economic opportunity can be fostered with the neighbourhood’s transition.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.002 |
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".