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Record W2137074584 · doi:10.1111/1475-6765.12090

Public support for European defence: Does strategic culture matter?

2015· article· en· W2137074584 on OpenAlexaff
Bastien Irondelle, Frédéric Mérand, Martial Foucault

Bibliographic record

VenueEuropean Journal of Political Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversité de Montréal
FundersEuropean Commission
KeywordsEurobarometerTypologyNational securityExplanatory powerPublic opinionGlobalismPolitical sciencePower (physics)SociologyPublic administrationEconomicsLawPoliticsEuropean unionInternational tradeEpistemology

Abstract

fetched live from OpenAlex

Abstract This article identifies previously ignored determinants of public support for the European Union's security and defence ambitions. In contrast to public opinion vis‐à‐vis the EU in general, the literature on attitudes towards a putative European army or the existing Common Security and Defence Policy (CSDP) suggests that the explanatory power of sociodemographic and economic variables is weak, and focuses instead on national identity as the main determinant of one's support. This article explores the possible impact of strategic culture, and argues that preferences vis‐à‐vis the EU's security and defence ambitions are formed in part through pre‐existing social representations of security. To test this proposition, ‘national’ strategic cultures are disaggregated and a typology is produced that contains four strategic postures: pacifism, traditionalism, humanitarianism and globalism. Applying regression analysis on individual‐level Eurobarometer survey data, it is found that strategic postures help explain both the general level of support for CSDP and support for specific Petersberg tasks.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.341
GPT teacher head0.455
Teacher spread0.114 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

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