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Record W7076961380

Нерівномірність оборонних витрат серед країн-членів альянсу: виклики в умовах розширення

2025· article· en· W7076961380 on OpenAlexaboutno aff

Bibliographic record

VenueScientific periodicals of Ukraine · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Member statesModernization theoryPoliticsDistribution (mathematics)AccessionFinancial crisis
DOInot available

Abstract

fetched live from OpenAlex

The article provides a theoretical and methodological justification and empirical analysis of the uneven distribution of defence spending among NATO member states in the context of the Alliance’s expansion, particularly in terms of the fair distribution of the financial burden, the institutional responsibility of participating states, and the impact of asymmetries on the effectiveness of the collective security system. Significant asymmetry in member countries’ contributions has been identified, leading to ongoing political debate on the issues of «fair burden sharing» and the effectiveness of the current financing model. Key milestones in NATO’s expansion have been examined, starting with the first phase of large-scale expansion in 1999. It has been determined that the accession of new members required adaptation to NATO financial standards, in particular the revision of defence financing structures, the modernisation of armed forces and the achievement of agreed budgetary targets. The defence expenditure of NATO countries as a percentage of GDP in 2024 has been analysed. It has been determined that a significant number of NATO member states, primarily from Central and Eastern Europe, demonstrate relatively low absolute levels of defence spending compared to leading Western allies. The dynamics of defence spending in the United States, Europe and Canada in 2014–2024 have been calculated. The total defence spending of NATO member states in 2024 has been calculated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.252
Teacher spread0.249 · 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.

Study designTheoretical or conceptual
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

Explore more

Same venueScientific periodicals of UkraineSame topicTheoretical and Computational PhysicsFrench-language works237,207