Transnational Urban Planning in the Multicultural City: An Analysis of Diversity Beyond Ethnoculturalism
Bibliographic record
Abstract
Multiculturalism policy in Canada was intended to create a greater acknowledgement of the diverse contributions made by migrants. The federal government’s policy framework sought to have diverse migrants in Canada included within government initiatives and public participation. A critical aspect to multiculturalism has been a focus on ethnoculturalism. However, it has become increasingly evident that multiculturalism has failed to address widening levels of inequity and inequality, most notably in the city of Toronto. Multiculturalism has also insufficiently enabled a broader public participation with diverse migrants. This study adopts a qualitative approach to understand migrant diversity beyond ethnoculturalism. By conducting 5 semi-structured interviews and reviewing relevant scholarly and grey literature, this paper considers a transnational framework to look at public engagement through multicultural urban planning and question its focus on ethnoculturalism. My research reveals that people’s experiences with trauma, violence, gender marginalization, or undemocratic institutions among others are not always considered in both multiculturalism and urban planning – therefore affecting the public participation process. I argue that planning practitioners must look beyond migrant ethnocultural diversity alongside the complex lived experiences across the globe and state borders. By recognizing this diversity, practitioners could begin to look at (re)igniting political activism among migrants in the multicultural city.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".