Conceptualizing anti-Asian racism in Canada during the COVID-19 pandemic: A call for action to social workers
Bibliographic record
Abstract
Anti-Asian racism in Canada has emerged from the COVID-19 pandemic and become more rampant. This article integrates Canadian postcolonialism, a critique of Canadian multiculturalism, and a framework of intergroup prejudice to conceptualize the covert anti-Asian racism that is entrenched in Canadian society. How COVID-19 exposes and “legitimizes” anti-Asian racism is further analyzed and included in this conceptualization. This conceptualization also includes social workers’ leading roles in combating anti-Asian racism through reforming and integrating client interventions, cultural policy, social context, and offers directions that can guide future social work research and practice in improving social justice during this crisis.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.051 | 0.066 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".