Racial discrimination at the polls? The Canadian case of Jagmeet Singh
Bibliographic record
Abstract
Discriminatory attitudes towards racial minorities are prevalent throughout society. However, there is mixed evidence of electoral discrimination for racial minority candidates beyond the American context. This paper investigates the effect of racial attitudes on the electoral performance of Canada’s New Democratic Party (NDP), by examining the case of Jagmeet Singh, the country’s first major federal party leader of color. Relying on three surveys from the Canadian Election Study (2015–2021) and controlling for demographics, ideology, and partisanship, we find voters with more negative attitudes toward racial minorities were significantly: (1) less likely to vote for the NDP under Singh’s leadership; (2) more likely to abandon than join the party in his first federal election; and (3) more likely to view Singh negatively than his predecessor. The findings suggest that some racial minority candidates likely face significant electoral penalties, which may contribute to the consistent underrepresentation of racial minorities throughout democracies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".