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Record W4401616450 · doi:10.1080/08865655.2024.2382680

Competing Visions at the EU’s Edge: Liberal vs. Conservative-Christian Notions of Europe in the Ukrainian–Hungarian Borderland

2024· article· en· W4401616450 on OpenAlexvenueno aff
Péter Balogh

Bibliographic record

VenueJournal of Borderlands Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Politics and Security
Canadian institutionsnot available
FundersBritish Academy
KeywordsVisionUkrainianHegemonyOpposition (politics)Political sciencePolitical economyPoliticsGender studiesEuropean unionEthnic groupIdentity (music)SociologyLawAnthropologyAestheticsEconomics

Abstract

fetched live from OpenAlex

Across Europe there is a plurality of competing visions on what Europe means, each appealing to identity and bringing along potential (geo)political implications. Identification with such visions are particularly worth investigating along the EU external boundary, some sections of which divide ethno-linguistic and religious communities. This paper is based on interviews with 23 elites in the Ukrainian–Hungarian borderland conducted some weeks before Russia’s full-scale invasion of Ukraine. The Ukrainian responses were marked by an overridingly positive view on Europe, while those of the Hungarians on both sides of the border were overall clearly more skeptical, with attitudes strongly overlapping with current hegemonic discourses on the EU in Hungary. The paper points to the salience of ethnic identities and the resonance of narratives in Budapest and Kyiv among these borderland communities. It also draws out tensions between the benefits of EU membership and a Hungarian national identity increasingly framed in opposition to the Union.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.013
Scholarly communication0.0080.003
Open science0.0000.005
Research integrity0.0010.002
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 head0.046
GPT teacher head0.356
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 designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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