Governing the Commercial Streets of the City: New Terrains of Disinvestment and Gentrification in Toronto's Inner Suburbs
Bibliographic record
Abstract
Abstract This paper explores the commercial shopping street as a site of racialized class struggle. The argument builds around the study of a disinvested inner‐suburban neighbourhood in Toronto, which furnishes an ideal case through which to achieve the paper's objectives of, first, identifying commercial space as an important site of contestation over competing suburban futures; second, delineating how processes of racialization inform the economies of commercial gentrification and urban renewal; and third, highlighting the epistemological and theoretical insights that emerge when research is conducted collaboratively, among academic, community, and activist groupings. The paper argues that such commercial spaces play a key role in making the city accessible to vulnerable and marginalized groups. Two competing planning agendas centred on reordering commercial space, meanwhile, spell the almost‐certain demise of such arrangements: a “real estate” vision featuring new condominium developments, and a new urbanist resistance favouring “green” and “creative” alternatives. Our engagements with precarious, predominantly immigrant‐owned businesses and community‐based researchers reveal the complicity of both modes of development planning with processes of displacement and structural racism. Specifying these dynamics as “racialized class projects” opens up space for intervention and organizing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".