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Record W4323667261 · doi:10.1080/1331677x.2023.2167222

The stimulus of European Union accession on the personal values formation process: a study of Croatia and Slovenia

2023· article· en· W4323667261 on OpenAlexaff
Marina Dabić, Carolyn P. Egri, Vojko Potočan, Zlatko Nedelko

Bibliographic record

VenueEconomic Research-Ekonomska Istraživanja · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAccessionEuropean unionStimulus (psychology)Political scienceBusinessPsychologyInternational tradeCognitive psychology

Abstract

fetched live from OpenAlex

The main purpose of this study is to investigate the change in the personal values orientations of individuals in Croatia and Slovenia resulting from the countries’ accession to the European Union (EU). We examined business managers’ and professionals’ value orientation by using four individual-level higher-order dimensions of self-transcendence, self-enhancement, openness to change and conservation, as defined in Schwartz’s value theory. To capture the effect of EU accession, we examined employees’ values orientation before accession to the EU (Croatia N = 276; Slovenia N = 389) and after each country’s accession (Croatia N = 223, Slovenia N = 336). This study reveals a substantial impact of this major socio-political change on the individual value-formation process. The value-formation of Croatia and Slovenia poorly follows manifested EU common principles and shared values, where Slovenians have more aversive look at the EU integration, then Croatians, what can be assigned to ‘initial enthusiasm', as Croatia entered almost decade later. The identified ‘EU integration gap' warns that accession to the EU is more associated with reaping economic benefits than with aligning the country’s values with those emphasized by EU integration. The findings have important implications for value management in the EU, single countries, and organizations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.237
GPT teacher head0.455
Teacher spread0.218 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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