Understanding the Electoral Participation Gap: A Study of Racialized Minorities in Canada
Bibliographic record
Abstract
Racialized minorities constitute an increasingly substantial segment of modern electorates in Western democracies, in part driven by immigration. Analyzing data from the 2021 Canadian Election Study (N = 9,496) and yearly Democracy Checkup surveys between 2020 and 2023 (N = 26,908), we explore the significance of racial identity as a determinant of voter turnout. Our findings reveal stark disparities in electoral participation between the most racialized minority groups in Canada and the White majority. Except for Latino identifiers, Indigenous, Asian, Black, and Arab-identifying respondents all exhibit lower voting rates, with Black voters facing the most significant gap, nearly 16 percentage points below their White counterparts. The gap is particularly prominent among second-generation racialized Canadians, suggesting that newcomers to Canada exhibit relatively high levels of engagement compared to their children. Next, we explore three key individual factors that may contribute to the gap: differences in socioeconomics, psychological engagement, and mobilization and community embeddedness. We employ a linear decomposition technique to assess the contributions of these factors to the majority–minority participation gap. Our analysis underscores the potency of socio-economic and psychological models in explaining minority under-participation in the Canadian context. The mobilization and community embeddedness model, however, exhibits weak explanatory power. Despite these insights, a substantial portion of the participation differentials remains unexplained, suggesting the necessity for novel perspectives to understand gaps in the electoral participation of racialized electors.
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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 | 0.000 | 0.000 |
| 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".