Making Space for Ethnic Culture: Examining how municipalities plan for ethnocultural infrastructure through the case of banquet halls serving South Asians in the Greater Toronto Area
Bibliographic record
Abstract
Despite being one of the most ethnically diverse metropolitan regions in Canada, urban planning in the Greater Toronto Area has not responded to the ethno-cultural practices of local communities (Viswanathan, 2009). Heritage and cultural planning practices have privileged tangible and civic culture, and planners have primarily responded to the ethno-cultural needs of communities through ad-hoc accommodations (M. A. Qadeer, 2009). Starting from this critique, this research project studies the empirical case of privately-owned event spaces serving South Asian communities in Toronto, Brampton, and Mississauga as ethno-cultural infrastructure that are used for gathering, celebration, and the passing of rituals and traditions on to future generations. Most of these spaces are currently relegated to “employment areas” that are zoned for industrial and commercial uses. New provincial regulations are shifting how these employment zones are planned and regulated, thus putting the future of these event spaces at risk. Event spaces are currently not identified as being culturally-important in any of these municipalities. By engaging with the owners and operators of such spaces, this project illustrates how these spaces operate and are used, the role they play in the ecosystem of ethno-cultural production, and how they serve the specific event-based needs of various South Asian communities. In comparison, through interviews with municipal planners working in zoning, cultural planning, and heritage planning, this project also identifies how these places are currently conceived of only as event venues (or places of assembly per zoning definitions), devoid of their cultural meaning by municipal and provincial planning systems. An analysis of provincial and municipal heritage and cultural planning policies supplement this finding through identifying how civic culture, creative industries, and tangible (often white, settler-colonial) heritage is codified in municipal planning. Through exploration and critique of the tools available to heritage and cultural planners, this project proposes a series of interventions, both conceptual and procedural, that advocate for consideration of ethno-cultural spaces, such as these event spaces, in planning practice. This research aims to bring the stories of banquet halls into scholarly and public dialogue and emphasize their importance as ethno-cultural infrastructure which needs to be preserved for future generations through active intervention by urban planners.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".