Racial Cues and Attitudes toward Redistribution: A Comparative Experimental Approach
Bibliographic record
Abstract
Support for welfare in the US is heavily influenced by citizens’ racial attitudes, especially citizens’ attitudes toward Blacks. Indeed, the fact that many Americans think of welfare recipients as poor Blacks (and especially poor Black women) is a common explanation for Americans’ comparatively low support for redistribution cross-nationally. 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 with representative samples (n=1200) in each of the US, UK and Canada. Relying on a series of survey vignettes, we manipulate program type (welfare vs. unemployment insurance) as well as the ethno-racial background of recipients (through morphed photos and common ethnicized names). In doing so, we seek to make three specific contributions. First, we test whether support for a means-tested program like welfare is lower than support for contribution-based program like unemployment insurance. Second, we extend the American literature to explore whether there is an anti-Black bias in other countries. Third, we examine whether citizens respond to other minority groups (Asians and Southeast Asians) in a similar manner. Parallel survey designs allows for an unprecedented comparative analysis of the underlying political-psychological sources of support (or lack of support) for redistributive policies across Anglo-Saxon democracies. The paper concludes by considering the implications of this study in light of growing immigrant-driven diversity in North America and Europe.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".