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Record W2007289705 · doi:10.1177/0020715210379456

Foundations of anti-immigrant sentiment: The variable nature of perceived group threat across changing European societies, 2002-2006

2010· article· en· W2007289705 on OpenAlexvenueno aff
Florian Pichler

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationBiology and political orientationEuropean Social SurveyPoliticsSocial psychologyPolitical scienceVariable (mathematics)Immigration policyDemographic economicsDevelopment economicsPolitical economyPositive economicsEconomicsPsychologyLaw

Abstract

fetched live from OpenAlex

In this article we examine how Europeans perceive the consequences of immigration. We draw upon group threat, conflict and boundary-making theories to differentiate between probable reasons of anti-immigrant sentiment. We hypothesize that perceived threats vary over time and across countries since their nature may shift according to changing economic and other conditions. Using data from three rounds and 24 countries of the European Social Survey, perceived threat is explained by socio-economic characteristics, political orientation and structural conditions. We then focus on more specific threats in economic and cultural terms and how they vary in their effects on negative attitudes toward immigration. Our findings support variable economic and cultural foundations of anti-immigrant sentiment exerting different influence associated with changing economic conditions. Implications of these findings for future policy and research are discussed in the light of the current economic crisis.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.359
Teacher spread0.336 · 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

Citations74
Published2010
Admission routes1
Has abstractyes

Explore more

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