Planning for Diversity in a Suburban Retrofit Context: The Case of Ethnic Shopping Malls in the Toronto Area
Bibliographic record
Abstract
Recent waves of immigration have had a dramatic impact on urban economies and the landscapes of Canadian metropolitan regions. With increasing suburbanization of immigrant settlement, ethnic shopping malls have emerged as a noticeable phenomenon in suburban regions of the Greater Toronto Area (GTA). The dynamics of ethnic retailing generate significant community changes and raise questions for planners in terms of land use, built form, parking capacity, economic development, and community building. This chapter investigates the development and retrofit processes of several ethnic shopping malls located at the major intersection of Steeles Avenue East and Kennedy Road bordering the City of Toronto and the City of Markham. Specifically, it examines how these ethnic malls were developed since the 1990s in response to the growing Chinese population in the area and the booming Asian-oriented businesses and how they successfully regenerated the area once affected by business decline but also presented unprecedented challenges to the planning system. The chapter also examines the evolving role of city planners in facilitating the new retail form and addressing the challenges it poses, followed by a summary of lessons learned. The findings presented in this chapter reveal that the suburban ethnic mall is not a stand-alone phenomenon; instead, it should be treated as an important part of the community. Planners must think beyond technicalities and exert more control on the free market in order to help nurture and sustain the emerging ethnic market that can, in turn, be a lucrative tool for the larger economy and contribute to community-building.
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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 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".