Why are minorities poor? Cross-Atlantic explanations for poverty and public support for redistribution
Bibliographic record
Abstract
The article describes public explanations of economic deprivation among minorities and their correlation with support for redistribution. The point of departure is the well-established American case of majority perceptions of Black people, which we compare with majority perceptionsof Black people in Canada and Muslims in the UK, France, Denmark,Sweden and Italy. The study draws on original survey data collected in each country in 2021-2022 and finds that poverty among Muslims incontinental Europe is more assigned to laziness and lack of will power and less assigned to discrimination than is the case for Black people in the US. In contrast, poverty among Muslims in the UK and Black people in Canada is less assigned to a “deviant” work ethic and equally assigned to discrimination than is case for Black people in the US. Across all countries, the article finds these explanatory modes are correlated with support for redistribution to the minority in question, even controlling forpolitical orientations and a range of other relevant deservingness criteria,and “spill over” to general redistributive preferences. This indicates the general challenge from the presence of economically deprived minoritieson support for distribution. However, our results also indicate that explaining poverty with discrimination of ethnic minorities is a substantial driver of generating support for redistribution.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".