Overestimating Reported Prejudice Causes Democrats to Believe Disadvantaged Groups Are Less Electable
Bibliographic record
Abstract
Four studies show that Democrats overestimate the explicit prejudice reported by the American electorate, leading them to perceive presidential candidates from disadvantaged groups as less electable. Study 1 (MTurk; n = 728) found that Democrats overestimated the percentage of Americans who say they would not vote for presidential candidates from disadvantaged groups. Study 2 (MTurk; n = 597) replicated this finding and demonstrated that Democrats who perceive high levels of explicit prejudice toward a group also believe presidential candidates from that group are less electable. Moreover, Democrats who more frequently interacted with Republicans were more accurate in estimating the amount of explicit prejudice reported by Republicans, Democrats, and Americans in general. Studies 3A (Prolific; n = 930) and 3B (YouGov; n = 747) found that presenting information about true levels of reported prejudice made Democrats believe generic presidential candidates from disadvantaged groups would be more electable. We did not find evidence that information about true levels of reported prejudice affected Democrats' beliefs about the electability of specific candidates in the 2020 Democratic Primary or their support for these candidates.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".