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Record W4402423666 · doi:10.1080/1369183x.2024.2401040

Cutting in line ahead of us: the role of group relative deprivation in shaping gatekeeping attitudes across different immigrant integration contexts in Europe

2024· article· en· W4402423666 on OpenAlexfundno aff
Duygu Merve Uysal, Sedef Turper

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersInstitute of Population and Public HealthTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsGatekeepingImmigrationRelative deprivationGroup (periodic table)Social psychologyDemographic economicsPolitical scienceLine (geometry)PsychologyGender studiesSociologyLawEconomics

Abstract

fetched live from OpenAlex

Although research shows that anti-immigrant sentiments are generally lower in liberal integration policy contexts, popular backlash against immigrants become salient in many pro-immigration and inclusive integration policy contexts in Europe today. Developing a contextualized mediation model, this research suggests that feelings of deprivation vis-à-vis immigrants influence attitudes toward selective immigrant admission in Europe. From a cross-country analysis of the 2014–2015 European Social Survey through multigroup structural equation modeling, our findings reveal that sentiments of group relative deprivation translate into stronger gatekeeping attitudes throughout Europe by developing threat perceptions from immigration. Relative deprivation-driven threat perceptions influence gatekeeping attitudes more potently in countries where integration policies grant immigrants more comprehensive and equal rights, while they remain relatively dormant in countries with exclusionary integration policies. These findings contribute to our understanding of how and to what extent relative deprivation sentiments vis-à-vis immigrants shape gatekeeping attitudes while shedding light on the unintended impacts of liberal integration policies on public opinion regarding immigration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.400
Teacher spread0.328 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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