Нерівномірність оборонних витрат серед країн-членів альянсу: виклики в умовах розширення
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".