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Record W4281651742 · doi:10.5539/res.v14n2p145

Acceptance of European Values: Case Study Dr. Franjo Tuđman Croatian Defence Academy

2022· article· en· W4281651742 on OpenAlexvenueno aff
Andrija Kozina

Bibliographic record

VenueReview of European Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyOfficerCurriculumPolitical scienceLegislatureAutonomyPublic administrationCroatianState (computer science)SociologyLawPedagogyPhilosophy

Abstract

fetched live from OpenAlex

The paper analyses the acceptance of selected democratic values by the students at the first, second, and third levels of progressive and sequential officer education at Dr. Franjo Tuđman Croatian Defence Academy and the understanding of a democratic society. The degree of the students' acceptance of nineteen values derived from the values of the European democratic system is examined. It includes students' acceptance of multi-party democracy, legislature, economy, living standard, human and civil rights, autonomy, private, public, and state property, as well as co-existence. The data collected during the process of preparation of the doctoral dissertation titled “Intercultural curriculum of military schools” were used. The dissertation was defended in 2018 at the Faculty of humanities and social sciences, University of Zagreb. The survey found that the level of military education is related to the level of acceptance of European democratic values. Students at higher levels of officer education show a higher degree of acceptance of selected democratic values and have a more positive attitude toward them.

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)0.008
metaresearch head score (Gemma)0.005
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.400
Teacher spread0.270 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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