How Xenophobia Shapes Political Party Support: Evidence from COVID-19 in Canada
Bibliographic record
Abstract
Racialized or ethnically marginalized groups typically have strong loyalties to particular political parties, but can these group loyalties be undermined? In this paper, I investigate whether racist but group-specific political discourse can alter these loyalties by activating a sense of linked fate among those who share a panethnic identity (e.g., as Asian). Using Canadian Election Study data in a quasi-experimental research design, I explore the impact of the highly visible, anti-Asian racism during the COVID-19 pandemic and whether this led to changes in political party support among different Asian ethnic groups, relative to the control, in Canada. I find that Conservative Party support declined more steeply for Chinese respondents than for any other Asian communities after the pandemic, despite Chinese being most likely to vote Conservative pre-pandemic. Therefore, I argue that periods of widespread discrimination can lead people to reject parties that are exclusionary against their group. However, despite their shared vulnerability to discrimination, this rejection of the Conservative party did not occur among all those who are racialized within Asian panethnic identity. Hence, racially hostile but group-specific language can potentially undermine a sense of linked fate and collective political action as a result. Supplementary Information: The online version contains supplementary material available at 10.1007/s12552-025-09480-y.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".