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Record W2100127909 · doi:10.1177/0020715208088911

Support for Repatriation Policies of Migrants

2008· article· en· W2100127909 on OpenAlexvenueno aff
Marcel Coenders, Marcel Lubbers, Peer Scheepers

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsRepatriationEuropean Social SurveyImmigrationSocializationEthnic groupPolitical scienceDemographic economicsDevelopment economicsSociologyEconomicsLawSocial sciencePolitics

Abstract

fetched live from OpenAlex

In this article we focus on the acceptance of migrants among the general public in the receiving societies. We analyze the most radical of such anti-immigrant sentiments, that is, the support for repatriation policies for legally established immigrants. We analyze intra- and international differences among Western and Eastern European societies, taking advantage of recently collected cross-national high quality data providing means to rigorously test hypotheses on individual and contextual level determinants. Although there are large differences between countries within European regions, we found that support for repatriation policies is overall somewhat higher in Western European societies. In line with Ethnic Group Conflict Theory, support for repatriation policies is stronger in countries with higher proportions of resident migrants and higher levels of immigration. Regarding individual level determinants, we found that particularly lower educated individuals are more in favor of repatriation of migrants. The effect of education differs however across countries and is — in line with socialization theories — less strong in Eastern European countries.

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)0.008
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.086
GPT teacher head0.425
Teacher spread0.340 · 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

Citations47
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Comparative SociologySame topicMigration, Refugees, and IntegrationFrench-language works237,207