Holding Back the Race Card: Black Candidates, Twitter, and the 2021 Canadian Election
Bibliographic record
Abstract
Abstract Politicians carefully construct a public persona that is authentic to who they are as individuals but also addresses voter expectations. Many Black candidates follow a deracialization strategy in which they downplay their racial identities to seek voter support while some follow a racial distinction strategy in which they highlight their racial identities but situate them within hegemonic national narratives. But questions remain about whether a candidate’s decision to use one strategy over another is shaped by national context, partisanship, political position, and riding competitiveness. This paper thus asks the question: How do Black candidates in Canadian elections deploy race in their campaign communications, and what factors might explain any differences in their strategies? To answer this question, we analyze how Black candidates used Twitter during the 2021 Canadian election. Our analysis reveals that Black candidates generally used a deracialization strategy when communicating on Twitter, opting to celebrate the many cultural groups in their riding rather than casting their appeal only to Black voters. They only highlighted their racial identities or racial issues when world or campaign events gave them the political cover to do so. But the degree to which Black candidates engaged in (de)racialized communications differed by party.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".