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Record W4214866815 · doi:10.1017/pls.2022.6

Disgust sensitivity and support for immigration across five nations

2022· article· en· W4214866815 on OpenAlexaboutno aff
Scott Clifford, Cengiz Erişen, Dane Wendell, Francisco Cantú

Bibliographic record

VenuePolitics and the Life Sciences · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
Fundersnot available
KeywordsDisgustImmigrationOpposition (politics)PoliticsSocial psychologyPsychologyDiseasePolitical scienceDevelopment economicsMedicineEconomicsPathology

Abstract

fetched live from OpenAlex

Immigration has become a focal debate in politics across the world. Recent research suggests that anti-immigration attitudes may have deep psychological roots in implicit disease avoidance motivations. A key implication of this theory is that individual differences in disease avoidance should be related to opposition to immigration across a wide variety of cultural and political contexts. However, existing evidence on the topic has come almost entirely from the United States and Canada. In this article, we test the disease avoidance hypothesis using nationally representative samples from Norway, Sweden, Turkey, and Mexico, as well as two diverse samples from the United States. We find consistent and robust evidence that disgust sensitivity is associated with anti-immigration attitudes and that the relationship is similar in magnitude to education. Overall, our findings support the disease avoidance hypothesis and provide new insights into the nature of anti-immigration attitudes.

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)0.005
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.330
Teacher spread0.246 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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