The Regulatory and Legal Regulation of Social Protection of Military Personnel in Ukraine and Foreign Countries
Bibliographic record
Abstract
The current global security situation, especially in light of ongoing conflicts, raises the importance of this topic from a purely academic interest to a matter of national strategic significance. Effective social protection directly impacts the morale of the military, recruitment processes, personnel retention, and the overall defense capability of the country. The aim of the article is a comprehensive analysis of the legal regulation of social protection for military personnel in Ukraine and a comparative study with the experiences of leading foreign countries (the United States of America, the United Kingdom, Germany, Canada) to reveal efficient models and formulate scientifically grounded recommendations for improving Ukrainian legislation and practices. The article conducts a thorough analysis of the legal regulation of social protection for military personnel in Ukraine in the context of modern security challenges, including war and martial law. The authors surveyed the legislative framework that provides social guarantees for military personnel and their families, highlighting key acts and subordinate normative documents. Particular attention was given to comparing the Ukrainian system with social protection models in leading foreign countries – the USA, the UK, Germany, and Canada. The levels of material, medical, and housing support, as well as the availability of psychological support and reintegration programs into civilian life, were analyzed. A number of problems in the Ukrainian model were identified: the fragmentation of legislation, lack of interagency coordination, instability of the normative framework, and limited adaptation and rehabilitation programs. Suggestions for improvement were proposed, including the codification of acts, the creation of unified centers for military support, and more active engagement of public organizations. The article outlines the prospects for transferring the best practices of Western countries to the Ukrainian context to ensure comprehensive protection for those participating in national defense.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".