Ethnic affinities and political engagement: an experimental study of Chinese-Canadian candidates and voters
Bibliographic record
Abstract
This article examines the affinity attitudes of Chinese-Canadian voters using a conjoint experimental design. We ask the following question: do Chinese individuals express more interest and feel more represented by Chinese candidates compared to non-Chinese individuals? And are such effects mediated by personal demographics and attitudinal factors? We know little about affinity attitudes among Chinese Canadians, but evidence indicates that they tend to be less politically engaged. We thus situate our research within the theoretical debate about the affinity model of political engagement: the idea that members of a minority group become more political engaged when they see “one of their own” running for office. Our findings reveal the presence of Chinese voter-candidate affinity but also of significant heterogeneity among Chinese respondents. The importance of ethnic identity and personal experience and perceptions of discrimination increased voter-candidate affinity. A significant gender gap also emerged. These results are the first step in a deeper examination of social diversity and affinity effects for Chinese and other racialized groups in ethnically diverse contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".