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Record W4416851030 · doi:10.1177/00207152251389091

National identity, support for democracy, and the mediating role of civic beliefs and participation

2025· article· en· W4416851030 on OpenAlexvenueno aff
Daniel Gabrielsson, Mikael Hjerm, Maureen A. Eger

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsNational identityMediationIdentity (music)Value (mathematics)Social identity theorySurvey data collectionDemocracyEuropean Social Survey

Abstract

fetched live from OpenAlex

This article explores the social mechanisms connecting national identity and support for democracy. Our investigation centers on the mediating role of civic beliefs and participation, employing data sourced from the European Value Survey (EVS 2017), which encompasses a total of 36 countries. First, our findings reveal a positive effect of voluntary (civic) national identity and support for democracy and a negative effect of non-voluntary (ethnic) national identity on support for democracy. Second, mediation analysis shows that individuals with higher levels of voluntary national identity exhibit stronger civic beliefs and participation, contributing to pro-democracy attitudes. In contrast, we find that non-voluntary national identity is inversely associated with civic beliefs and participation, largely explaining the negative effect on support for democracy. Although the mediators do not entirely account for the relationship between national identity and pro-democracy attitudes, they are important in shaping the relationship.

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)0.007
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.049
GPT teacher head0.463
Teacher spread0.414 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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