Race, prejudice and attitudes toward redistribution: A comparative experimental approach
Bibliographic record
Abstract
Abstract Past work suggests that support for welfare in the United States 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’ lukewarm support for redistribution. This article draws on a new online survey experiment conducted with national samples in the United States, the United Kingdom and Canada, designed to extend research on how racialised portrayals of policy beneficiaries affect attitudes toward redistribution. A series of innovative survey vignettes has been designed that experimentally manipulate the ethno‐racial background of beneficiaries for various redistributive programmes. The findings provide, for the first time, cross‐national, cross‐domain and cross‐ethno‐racial extensions of the American literature on the impact of racial cues on support for redistributive policy. The results also demonstrate that race clearly matters for policy support, although its impact varies by context and by the racial group under consideration.
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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 | 0.019 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".