Socio-structural Injustice, Racism, and the COVID-19 Pandemic: A Precarious Entanglement among Black Immigrants in Canada
Bibliographic record
Abstract
As several commentators and researchers have noted since late spring 2020, COVID-19 has laid bare the connections between entrenched structurally generated inequalities on one hand, and on the other hand relatively high degrees of susceptibility to contracting COVID-19 on the part of economically marginalized population segments. Far from running along the tracks of race neutrality, studies have demonstrated that the pandemic is affecting Black people more than Whites in the U.S.A. and U.K., where reliable racially-disaggregated data are available. While the situation in Canada seems to follow the same pattern, race-specific data on COVID-19 are hard to come by. At present, there is no federal mandate to collect race-based data on COVID-19, though, in Ontario, at the municipal level, the City of Toronto has been releasing such data. This paper examines the entanglements of race, immigration status and the COVID-19 pandemic in Canada with particular emphasis on Black immigrants and non-immigrants in Toronto, using multiple forms of data pertaining to income, housing, immigration, employment and COVID-19 infections and deaths. Our findings show that the pandemic has had a disproportionate negative impact on Black people and other racialized people in Toronto and, indeed, Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".