The Multicultural Welfare State: International Experience and North American Narratives
Bibliographic record
Abstract
Abstract Contemporary debates are increasingly pessimistic about the impact of ethnic diversity on support for the welfare state. A growing number of analysts argue that greater ethnic diversity in Western democracies is weakening public support for redistribution, and that this underlying tension is exacerbated by the adoption of robust multiculturalism policies. The purpose of this essay is to summarize early findings from several studies that bear on the questions at the heart of such debates. These studies analyse the implications of immigration and multiculturalism policies for the welfare state across OECD countries, and also focus more closely on the experience of two distinctively multicultural countries, the United States and Canada. The evidence points to more complex relationships than often assumed. OECD countries with large foreign‐born populations have not had more difficulty in sustaining their welfare states than other countries. The extent of change does seem to matter, however, as countries in which immigrant communities grew rapidly between 1970 and the late 1990s did experience lower rates of growth in social spending. But despite the warnings of some critics, robust multiculturalism policies do not systematically exacerbate this tension. Moreover, the United States and Canada reflect different patterns. In the US racial diversity does weaken support for redistribution; but Canadian experience suggests that immigration, multiculturalism policies and redistribution can represent a stable political equilibrium. These contrasting narratives from North America stand as a warning against premature conclusions based on the US experience alone. There is no inevitability at work, and policy choices do seem to matter.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.026 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".