The Negotiation of Space and Rights: Suburban Planning with Diversity
Bibliographic record
Abstract
The increasing suburbanization of immigrant settlement in Canada’s major receiving cities has created unprecedented challenges for municipalities. Despite emerging research about the rise of ethnic suburbs in Canada and abroad, the role of suburban municipalities in facilitating immigrant integration and planning with diversity remains unclear. Based on mixed-method ethnographic research, this article investigates how immigrant and racialized communities in the Greater Toronto Area have significantly transformed suburban places and built institutionally complete communities. However, the rapid development of these spaces has not been fully recognized or supported by municipal planning authorities. Conflicts related to land use, public engagement, and public realm development expose planning’s failure to keep pace with the diverse needs of immigrant communities, who must continually negotiate and fight for their use of space. Furthermore, the lack of effective civic engagement not only ignores immigrant and racialized communities as important stakeholders in suburban redevelopment, but also threatens to destroy the social infrastructure built by these communities and their ‘informal’ practices that are often not recognized by the planning ‘norm.’ Without appropriate community consultation, planning processes can further sideline marginalized groups. Lack of consultation also tends to prevent cooperation between groups, impeding the building of inclusive communities. It is imperative for municipalities to better understand and encourage community engagement and placemaking in ethnic suburbs. This study offers several recommendations for suburban planning with diversity.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.036 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| 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".