Multiculturalism, Racism and Infectious Disease in the Global City: The Experience of the 2003 SARS Outbreak in Toronto
Bibliographic record
Abstract
The 2003 SARS outbreak in Toronto, which killed forty-four and made hundreds sick, tested the multicultural model often presented as the reason for making that city a livable global metropolis. Billed as the “Chinese disease,” SARS connected seamlessly with previous periods of racializing disease assumed to originate from migrants and foreigners in North America. Yet when restaurants in the city’s three Chinatowns remained empty for weeks and close contact with Chinese citizens was avoided by others in public, the dynamics that unfolded also tied in with a new development in Toronto: the formation of the global city. As news on the SARS outbreak spread and the intricate details of travel patterns and infection-pathways became clearer, the relationships of Toronto diasporic communities and business ties with other globalizing cities like Hong Kong, Guangzhou and Singapore became obvious, and Toronto’s vulnerability in the network of global flows of finance, culture, commodities and people was exposed. Our paper provides a narrative of the racialization of infectious disease in the context of Toronto’s multiculturalism and the region’s formation as a major global city. Providing evidence of racialization in public discourse, everyday practices and institutional policies, we advance the hypothesis that the SARS outbreak strained the usually happy appearance of this particular multicultural urban fabric of diversity. This analysis is part of a long-term research project at York University on SARS and the Global City, which addresses the network connectivity of Toronto in the global city hierarchy; the influence of infectious disease; and the re-scaling of the health governance system in Toronto in the wake of the SARS outbreak.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".