Combating Anti-Asian Racism and Xenophobia in Canada: Toward Pandemic Anti-Racism Education in Post-COVID-19
Bibliographic record
Abstract
Canada is often held up internationally as a successful model of immigration. Yet, Canada's history, since its birth as a nation one hundred and fifty-four years ago, is one of contested racial and ethnic relations. Its racial and ethnic conflict and division resurfaces during cov id -19 when there has been a surge in racism and xenophobia across the country towards Asian Canadians, particularly those of Chinese descent. Drawing on critical race theory and critical discourse analysis, this article critically analyzes incidents that were reported in popular press during the pandemic pertaining to this topic. The analysis shows how deeply rooted racial discrimination is in Canada. It also reveals that the anti-Asian and anti-Chinese racism and xenophobia reflects and retains the historical process of discursive racialization by which Asian Canadians have been socially constructed as biologically inferior, culturally backward, and racially undesirable. To combat and eliminate racism, we propose a framework of pandemic anti-racism education for the purpose of achieving educational improvement in post-COVID-19.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".