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. The online version contains supplementary material available at 10.1007/s12552-025-09480-y.

Keywords

DiscriminatorGenerator (circuit theory)Generative grammarAdversarial systemSet (abstract data type)Computer scienceArtificial intelligenceArtificial neural networkOrder (exchange)Machine learningEconomicsTelecommunicationsFinanceProgramming language

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Publication Info

Year
2023
Type
book-chapter
Pages
73-76
Citations
19789
Access
Closed

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Cite This

Raphael Labaca-Castro (2023). Generative Adversarial Nets. Machine Learning under Malware Attack , 73-76. https://doi.org/10.1007/978-3-658-40442-0_9

Identifiers

DOI
10.1007/978-3-658-40442-0_9
PMID
41347119
PMCID
PMC12672673

Data Quality

Data completeness: 77%