How have Asians experienced discrimination differently during COVID-19? The role of nativity
Bibliographic record
Abstract
In this article, we consider differences in how native-born Asians and foreign-born Asians may have experienced rising anti-Asian attacks during the COVID-19 pandemic. We analyze Canadian data from a national survey (two waves conducted in April and December 2020) that includes a subsample of 464 Asians (native-born=178; foreign-born=286). Results from negative binomial regressions suggest that perception of anti-Asian racism is highly conditioned by nativity. Specifically, native-born Asians are significantly more likely than foreign-born Asians to report having encountered instances of acute discrimination during the pandemic. To explain the perceived discrimination gap, we test whether a stronger sense of cultural belonging and ethnic pride among native-born Asians contributes to their greater sensitivity to discrimination and thereby higher perceptions of discrimination. We measure sense of cultural belonging and ethnic pride using in-group trust (ethnic trust in Asian people). Although we do find native-born Asians show greater in-group trust, it does not seem to explain away the higher levels of discrimination perceived by native-born Asians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".