Anti-Asian racism during the coronavirus pandemic: the invisible epidemic
Bibliographic record
Abstract
Although everything has been slowly returning “back to normal”, the coronavirus pandemic has caused irreversible social and economic harms, chief among them the racial discrimination experienced by Asian people. Anti-Asian terms are more frequently seen in social media, and news articles and research indicate the disturbing escalation in verbal and physical assaults that Asian people have witnessed or suffered from. Grounded in critical race theory and intersectionality and using cross-national survey data from the COVIDImpacts.ca team in Canada, USA, and Mexico, this thesis examines, quantitatively, if and to what extent does the COVID-19 pandemic exacerbate racism against Asian people in Canada and the U.S. Findings from bivariate and logistic regression analyses reveal that Asians in both countries have higher odds of experiencing racial discrimination during COVID-19 compared to those with other socioeconomic statuses or identities, and Asians living in the U.S. are more likely to experience racial discrimination or more inclined to report such experience compared to those living in Canada. These results provide insight into the lived Asian experience during COVID-19 and shed light on the struggles that the Asian community has been facing since even before this pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".