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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".