Ethnicity and the 2019 Canadian federal election: do \nracialized candidates increase voter turnout?
Bibliographic record
Abstract
Voting behaviour research in Canada has shown that racialized voters tend to support racialized candidates and candidates with whom they share an ethnic identity. However this work doesn’t address why racialized \nvoters might be turning out to vote in the first place. Using data from the 2019 Canadian Election Study, this \nproject helps to bridge tha gap by examining the mobilizing effect that racialized candidates may have had \non racialized voter turnout during the 2015 and 2019 Canadian federal elections. South Asian candidates \nwere found to positively increase reported turnout of South Asian voters in 2015. Similarly, the presence of a Chinese candidate in 2015 led to increased reported turnout among Chinese voters and increased reported \nplans to vote in 2019. These findings suggest that the presence of racialized candidates was linked to \nincreased turnout and plans to vote among voters with the same ethnic identity. The analysis in this thesis \nprovides further insight into the dynamics of racialized participation in Canadian politics.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".