Diversity our strength? Exploring the influence of power dynamics on shaping suburban ethnic strips and place-making
Bibliographic record
Abstract
In Canada’s multicultural context, diversity is often celebrated in city branding and promotion, yet, it is not clear what diversity means, to whom, and whether the advantages of urban diversity are well acknowledged to support inclusive place-making. Situated in two of Toronto’s most diverse inner suburbs, this paper explores how diversity and place are perceived, produced, and experienced by local immigrant and racialized communities in obsolete and stigmatized strip mall environments, and how power dynamics are presented and influenced through the place-making process. The findings reveal that, although the population is culturally diverse and immigrants actively contribute to suburban place-making, municipal plans and policies do not prioritize immigrant communities and businesses. This results in ineffective public engagement and a disconnect with the diverse communities, thereby impacting the outcomes of ethnic places. The current city-wide Business Improvement Area program also requires a renewed focus on diversity and inclusion. Without equity and inclusion as fundamental considerations in city planning and engagement processes, there is no guarantee that diversity can be effectively leveraged as an urban asset or strength. This oversight can adversely affect the outcomes of ethnic place-making, a process that aims to empower the relationships between people and place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".