Race, class, or both? Responses to candidate characteristics in Canada, the UK, and the US
Bibliographic record
Abstract
Research suggests that voters use identity markers to infer information about candidates for office. Yet politicians have various markers that often point in conflicting directions, and it is unclear how citizens respond to competing signals – especially outside of a few highly stigmatized groups in the US. Given the relevance of these issues for electoral behavior and patterns of representation, this article examines the impact of intersectional identities and less intensely stigmatized markers in Canada, the UK, and the US. It does so using a survey experiment that varies the race (white/East Asian) and class background (higher/lower) of a candidate for office. I then compare results across the cases, examining willingness to vote for the candidate as well as assumptions about his ideological proximity, relatability, and potential contributions. In doing so, I build from past research suggesting that voter ideology likely shapes reactions to candidates from disadvantaged backgrounds. Results suggest that marginalized identity markers have relatively widespread effects among leftists and (to a lesser extent) centrists, but that, outside of the Canadian left, class seems to matter more than race. Overlapping marginalized identities, in turn, had little impact, with the lower-class white and East-Asian profiles eliciting similar reactions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".