Lest We Forget: Politics of Multiculturalism in Canada Revisited during COVID-19
Bibliographic record
Abstract
Since COVID-19, we have witnessed a rise in hate crimes and xenoracism globally. Some commentators on COVID-related racism claim that this hate is apolitical. We question this claim, and in this paper, we strive to reveal the underlying politics especially around the ramifications and impact of this hate on racialized (im)migrants and the multiculturalism ideal. Drawing from Foucault’s construct of biopolitics and using Canada as a case study, we wonder how Canadian multiculturalism, which is a source of national pride, has been politically constructed to serve white settler hegemony from its inception to the present. We link political debates around the emergence of a multiculturalism policy in 1971 to the recent debates on multiculturalism and immigration during the 2015 and 2019 federal elections, and the current COVID-19 related national border policies in 2020. Our critical analysis illustrates how immigrants and racialized minorities have been systemically positioned in our legislation as a site to demonstrate the politics of governance, often scapegoated for national unrest and questioned on the legitimacy of their belonging and contribution to the nation. Meanwhile, the very ideal of multiculturalism in Canada has been evoked as the centre of biopolitics to govern ‘Others’ and all.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.087 | 0.044 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| 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".