Immigrant blaming during the COVID-19 pandemic: A Canadian American study
Bibliographic record
Abstract
The COVID-19 pandemic significantly impacted our society - economically, politically, and interpersonally. The Asian community, in particular, faced severe xenophobia globally, with blame often unfairly attributed to them for the COVID-19 pandemic. The present study focuses on factors associated with increased blame toward Asian immigrants by host nationals in the United States and Canada during the COVID-19 pandemic. As part of a research project on intergroup relations, data were collected from 233 native-born Americans and 218 native-born Canadians. Multiple regression analyses indicated that national identity and country of residence together explained seven percent of the variability in blaming Asian immigrants for the pandemic. Higher national identity was related to lower blaming of Asian immigrants. Conservative sociopolitical views were also associated with increased blaming. Additionally, we found a moderating effect of COVID-related worry on the relationship between country of residence and blaming Asian immigrants for the COVID-19 pandemic, suggesting that the relationship between the country of residence of the participants and the blame they place on Asian immigrants was influenced by their worry about the COVID-19 pandemic. These findings could be valuable in shaping public health communication strategies in future health crises, particularly in terms of ways to address and mitigate discriminatory discourse against minorities and immigrants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".