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Record W2914837734 · doi:10.1111/ssqu.12591

Economic Vulnerability, Cultural Decline, and Nativism: Contingent and Indirect Effects

2019· article· en· W2914837734 on OpenAlexaboutno aff
Nicholas T. Davis, Kirby Goidel, Christine S. Lipsmeyer, Guy D. Whitten, Clifford E. Young

Bibliographic record

VenueSocial Science Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological nativismImmigrationPerceptionVulnerability (computing)Political scienceDevelopment economicsPsychologyPolitical economySociologyEconomics

Abstract

fetched live from OpenAlex

Objective Nativism, or anti‐immigrant preferences, is increasingly evident within democratic mass publics. In this article, we explore whether economic concerns, perceptions of social decline, or some combination of the two shape these attitudes. Method Our data are comprised of more than 8,000 survey respondents from nine Western, industrial democracies (Australia, Canada, France, Germany, Great Britain, Italy, Spain, Sweden, and the United States). Using mediation analysis, we test for the extent to which economic anxiety and perceptions of cultural decline exert direct and indirect effects on nativism. Results On balance, the effect of economic anxiety—concern regarding job loss and negative economic assessments—on nativism is mostly indirect, affecting nativism by influencing perceptions of cultural decline. Conclusion Given the strong association between perceptions of social decline and nativism, it appears as though anti‐immigrant sentiments draw from cultural rather than economic concerns. The story, however, does not end there. Perceptions of societal decline are strongly influenced by economic anxiety.

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)0.002
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 score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.013
GPT teacher head0.323
Teacher spread0.310 · 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
Published2019
Admission routes1
Has abstractyes

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