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Record W4366251507 · doi:10.1080/17405629.2023.2200930

Strength of children’s European identity: findings from majority and minority groups in four conflict-affected sites

2023· article· en· W4366251507 on OpenAlexfundno aff
Laura K. Taylor, Bethany Corbett, Edona Maloku, Jasmina Tomašić Humer, Ana Tomovska Misoska, Jocelyn Dautel

Bibliographic record

VenueEuropean Journal of Developmental Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastBritish Academy
KeywordsOutgroupEuropean unionIdentity (music)Ingroups and outgroupsPsychologySocial psychologyGroup conflictDevelopmental psychology

Abstract

fetched live from OpenAlex

The European Union (EU) aims to promote peace. This research investigates the saliency of a European identity for children from majority and minority groups in four conflict-affected societies in Europe (Croatia, Kosovo, Northern Ireland (NI), and Republic of North Macedonia (RNM)). These sites represent a range of relations with the EU (e.g., leaving the EU, an EU member, wanting to join the EU). Participants included 442 children aged 7 to 11 years, evenly split by gender and group status (Croatia n = 90; Kosovo n = 107; NI n = 60; RNM n = 185). After a draw-and-tell task to prime European identity (vs. ingroup or control condition), we measured children’s identification with Europe, outgroup attitudes and prosociality. Although the European identity prime was not effective, children’s strength of European identity varied by site and group status and related to more positive attitudes and prosociality towards the conflict-rival outgroup. Implications for the future of the European project are discussed.

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)0.003
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.326
Teacher spread0.283 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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