Racial Cues, Prejudice and Attitudes Toward Redistribution: A Comparative Experimental Approach
Bibliographic record
Abstract
Past work suggests that support for welfare in the US is heavily influenced by citizens’ racial attitudes. Indeed, the idea that many Americans think of welfare recipients as poor Blacks (and especially as poor Black women) has been a common explanation for Americans’ comparatively low support for redistribution. In this study, we extend existing work on how racialized portrayals of recipients affect attitudes toward redistribution. The data for the analysis are drawn from a new and unique online survey experiment, implemented by YouGov Polimetrix and conducted with national samples in the US, UK and Canada. Through a series of survey vignettes, we experimentally manipulate the ethno-racial background of policy beneficiaries for various types of redistributive programs. By exploring whether citizens respond to other minority groups (Asians, Hispanics, South Asians and Native Americans) in a manner similar to Blacks, our results provide a cross-national extension of the American literature. We also provide an extension across policy domains by examining whether racial cues affect other welfare state policy domains. Finally, we also consider the relative impact of three different measures of overt, modern, and implicit racism.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".