MétaCan
Menu
Back to cohort
Record W2046330977 · doi:10.1177/0958928711418852

The effect of integration and social democratic welfare states on immigrants’ educational attainment: a multilevel estimate

2011· article· en· W2046330977 on OpenAlexaboutno aff
Flavia Fossati

Bibliographic record

VenueJournal of European Social Policy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationWelfare stateArgument (complex analysis)WelfareDemocracyMultilevel modelEducational attainmentDemographic economicsInequalityEuropean Social SurveySocial integrationSociologySocial stratificationPoliticsEconomicsPolitical scienceDevelopment economicsEconomic growthSocial scienceLaw

Abstract

fetched live from OpenAlex

Through the analysis of 22 European countries and Canada, this article seeks to investigate the assumption that political macro level variables such as welfare state systems and immigration regimes shape the conditions encountered by young immigrants and thus have an impact on their school performance. The results show that native students benefit from social-democratic welfare states and immigration-friendly integration regimes, whereas immigrant students underperform under these types of regimes. Thus, while the finding for native students supports the argument found in the body of literature, claiming that social-democratic welfare states lead to a reduction in inequality and to less stratification, the findings for immigrant students suggest that positive discrimination may under some circumstances lead to a counterproductive result. The argument is tested with a multilevel modelling procedure on three levels (student, school and country) based on different data sources.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.553
Threshold uncertainty score0.900

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of European Social PolicySame topicMigration, Refugees, and IntegrationFrench-language works237,207