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Record W4385075261 · doi:10.1177/00207152231188406

Europe’s internal migration and public support for income redistribution: The role of social protection

2023· article· en· W4385075261 on OpenAlexvenueno aff
Anne‐Marie Jeannet

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRedistribution (election)ImmigrationWelfare stateGenerosityEuropean Social SurveyEuropean unionRedistribution of income and wealthDemographic economicsPoliticsPolitical scienceEconomicsDevelopment economicsEconomic growthInternational tradeUnemploymentLaw

Abstract

fetched live from OpenAlex

Mass immigration is transforming the politics of income redistribution in European welfare states. Some scholars argue that immigration erodes public support for redistribution, while others argue it could have the opposite effect. Until now, the literature has attempted to isolate a generic role of immigration without distinguishing between different immigration categories. This article analyzes the relationship between internal European migration and public support for income redistribution in 17 Western European countries using the European Social Survey’s seven rounds (2002–2014). It finds that some forms of internal migration, namely, migration from new Central and Eastern European countries, are positively related to Western European support for income redistribution. The study also sheds light on the crucial role of the welfare state, finding that the compensation effect is stronger in countries with higher social protection. The results support group-specific understandings of the relationship between immigration and income redistribution. In sum, the relationship varies by immigrant group and depends on the generosity of social protection.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.081
GPT teacher head0.409
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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