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