Ethnic Identity, Place Marketing, and Gentrification in Toronto
Bibliographic record
Abstract
Urban theory has historically viewed ethnic commercial strips as a more-or-Iess organic extension of nearby ethnic residential enclaves. Although this is undoubtedly a useful way to describe such commercial spaces in many cities, this paper argues that some of these areas function as a branding mechanism (intended or not) to produce nearby residential gentrification. Certain forms of ethnic identity attract affluent professionals looking for an alternative to suburban life. Some neighbourhood institutions have recognized this attraction and begun to manufacture a saleable form of ethnicity to tourists and prospective residents alike. This paper explores the influence of ethnic packaging on the process of gentrification in Toronto, using the examples of four ethnically defined business improvement areas (BIAs) - Little Italy, Greektown on the Danforth, Corso Italia, and the Gerrard India Bazaar. These institutions actively manage and sell an ethnic identity that is increasingly at odds with nearby residential patterns. The commercial areas of these neighbourhoods now function less as areas of identification for the stated group, and more as ways to market each neighbourhood's residential real estate markets. In each case, the population of the stated group is declining, while efforts to market each neighbourhood as a niche to newcomers are increasing. Though no single pattern has resulted from these efforts, we found that packaged ethnicity is beginning to facilitate gentrification in those places already predisposed to the process. BIA officials are conscious of this connection and are actively using it to improve resources for their organization and their neighbourhood. This form of branding, and its connection to real estate valuation, has implications for gentrification theory and the study of urban landscapes.
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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 | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".