Sari vs. Dim Sum – Business Improvement Areas and the Branding of Toronto's Ethnic Neighbourhoods
Bibliographic record
Abstract
Abstract Under the umbrella of Toronto's city motto, ‘Diversity our Strength’, ethnically labelled Business Improvement Areas (BIAs) have become the object of branding strategies. While these branding processes generate tourist places and multicultural neighbourhoods for the creative and cosmopolitan, they challenge social cohesion. Branding often leads to urban revitalisation and thus causes the displacement of diverse communities and migrant enterprises through rising rents. Furthermore, ethnic place‐making and branding activity can create local conflicts around identity and urban images in which migrant agency plays a central role. This paper compares two ethnically‐branded BIAs in a political‐economy perspective to show that marketability between ethnic groups varies. It provides systematic analysis of urban policies towards the branding of migrant entrepreneurial neighbourhoods in Toronto. It further shows how heterogeneous power structures influence ethnic entrepreneurial neighbourhoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".