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Record W2942529318 · doi:10.1017/s1742058x07070038

WHO OPPOSES IMMIGRATION?

2007· article· en· W2942529318 on OpenAlexaboutno aff
Thomas F. Pettigrew, Ulrich Wagner, Oliver Christ

Bibliographic record

VenueDu Bois Review Social Science Research on Race · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationOpposition (politics)Immigration policyBiology and political orientationAlienationGermanPoliticsConservatismDemographic economicsAuthoritarianismPolitical sciencePopulationDominance (genetics)Social dominance orientationDevelopment economicsPolitical economyDemocracySociologyDemographyGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Are the predictors of anti-immigration attitudes consistent across countries with diverse immigration histories and policies? We hypothesize that the key predictors of opposition to immigration are indeed relatively consistent across industrial nations. We test this hypothesis with two surveys using probability samples of German citizens. We then compare our findings with those obtained in recent studies of immigration opinions in Europe generally, and in two of the world's leading immigration-receiving nations: Canada and the United States. Striking similarities emerge in the findings across structural, demographic, contact, economic, political, personality, and threat predictors. Opposition to immigration is routinely found strongest among the older and less-educated segments of the population who live in areas with anti-immigration norms and little contact with immigrants. Anti-immigration attitudes also correlate with political conservatism and alienation, economic deprivation, and especially with authoritarianism, social dominance orientation, and perceived collective threat.

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)0.007
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.136
GPT teacher head0.543
Teacher spread0.408 · 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

Citations116
Published2007
Admission routes1
Has abstractyes

Explore more

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