Accommodation and new hurdles: The increasing importance of politics for immigrants' access to social programmes in <scp>Western</scp> democracies
Bibliographic record
Abstract
Abstract Although immigrants' place in welfare state systems is of large relevance to academics and policymakers alike, there have been few attempts to compare immigrants' social rights in different countries at different moments in time systematically. This article presents the results from a comparative policy analysis that maps immigrants' access to seven different social programmes, in 20 different Western democracies, at four different points in time. The main findings are threefold. First, there are large differences in the extent to which different welfare states differentiate in benefit extension between immigrants and native‐born citizens. Second, over the last two decades, many countries have adjusted their welfare systems with the specific aim to accommodate immigrants, whereas many have also introduced punitive barriers that require immigrants to satisfy additional requirements. Third, these developments seem largely driven by politics: in particular, the adoption of punitive barriers has been more common in places where the political climate is more hostile to immigrants. These findings raise important questions about the future of social protection in an era of cross‐border mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".