Replication Data for: Projection in the Face of Centrism: Voter Inferences about Candidates’ Party Affiliation in Low-information Contexts
Bibliographic record
Abstract
When are voters more likely to project their own political position onto a candidate for office? We investigate this question by examining the assumed partisanship of a (self- declared) centrist politician, using data from a survey experiment fielded in Canada, the United Kingdom, and the United States. In doing so, we build on the social categorization model as well as recent U.S.- focused political science research on projection and ingroup/outgroup racial divides— extending our analysis to incorporate racial and class similarities/differences across three countries where these divides likely vary in salience. We thus seek: (1) to contribute to research on the inferences citizens draw in nonpartisan elections and low- information contexts generally and (2) to highlight some potential methodological complications of using partisanship- less candidates in vignette experiments. Results suggest that even in the face of a self- declared centrist, voters from across the political spectrum tended to assume shared partisanship in Canada, the United Kingdom, and the United States. Examining projection by ingroup/outgroup divisions indicated that class appears to shape projection across all three countries, but that the racial divide only mattered in the United States. Finally, we also find evidence of counterprojection toward outgroup members— but once again only in the American context.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.042 |
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".