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Record W4408281169 · doi:10.1080/13501763.2025.2475007

Embedded liberalism, economic nationalism, or Welfare Chauvinism? Experimental evidence on policy preferences in tough times

2025· article· en· W4408281169 on OpenAlexafffund
Leonardo Baccini, Mattia Guidi, Arlo Poletti

Bibliographic record

VenueJournal of European Public Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcGill University
FundersMinistero dell'Università e della RicercaUniversità degli Studi di TrentoSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsChauvinismNationalismLiberalismEconomicsWelfarePolitical economyPolitical scienceWelfare statePositive economicsEconomic nationalismSociologyEconomic systemLawMarket economyPolitics

Abstract

fetched live from OpenAlex

Three policy paradigms have emerged to address globalisation-induced economic vulnerabilities: (1) Embedded Liberalism (EL), (2) Economic Nationalism (EN), and (3) Welfare Chauvinism (WC). We investigate which of these policy paradigms is better equipped to address citizens' concerns in times of economic crises, by assessing which policies citizens prefer in response to negative economic shocks: (1) social expenditure and redistribution via taxation, (2) closing domestic markets to foreign products and people, or (3) social expenditure and redistribution via taxation and strict migration policies. Our key tests involves vignette experiments in the three largest EU economies: France, Germany, and Italy (N = 11, 000). We find that voters are more likely to support politicians who increase welfare spending. Follow-up conjoint experiments, which investigate specific attributes of social expenditure and redistribution, indicate strong support for social investment, progressive taxation, and extending social expenditure to both natives and foreigners. However, we show that right-wing respondents are significantly less likely to favour social expenditure for foreigners compared to centrist and left-wing ones. Our micro-foundational evidence suggests that, while politicians who advocate redistribution in tough times will enjoy a significant political advantage, citizens are ideologically divided as to whether welfare spending should come with an exclusionary component or not.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.074
GPT teacher head0.403
Teacher spread0.329 · 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 designTheoretical or conceptual
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

Citations3
Published2025
Admission routes2
Has abstractyes

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