COMPARATIVE ANALYSIS OF SOCIAL SERVICES PROVIDED TO FEMALE COMBATANTS AND VETERANS IN UKRAINE AND ABROAD
Bibliographic record
Abstract
The war in Eastern Ukraine has been going on since 2014. This situation has led to new challenges for the social sphere, in particular, the construction of a system of social services for combatants and veterans. Female combatants and veterans need gender-specific services adopted to this particular category. Since the experience of social assistance to this specific category of clients of social work in Ukraine is limited, there is a need to study the experience of other countries to address the issue.The purpose of the article is to analyze the peculiarities of women's military service and the system of social protection of this category in Ukraine and abroad.The article analyzes the experience of Australia, Israel, Spain, Canada, Korea, Poland, the USA, and Croatia in implementing the policy of social protection of combatants. The authors identify the main types of services and assistance to this category provided by the legislation in other countries.The authors of the article used the method of theoretical analysis of scientific works and legal documents that describe and regulate the mechanism of social protection of servicemen/servicewoman in different countries; compare the key aspects and features of women's military service in different countries and systematize the list of social services and structures responsible for providing various social services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".