Проблеми та перспективи економічної реінтеграції ветеранів війни: адміністративно-правовий підхід
Bibliographic record
Abstract
The article is devoted to a comprehensive analysis of the problems and prospects of economic reintegration of war veterans in the context of modern transformations of Ukrainian society. It is emphasized that the economic adaptation of veterans goes beyond the narrow social dimension and becomes a key factor in ensuring national security, social stability and sustainable economic development. In the context of large-scale armed aggression and post-war reconstruction, the problem of integrating former servicemen into the system of productive employment, entrepreneurship and professional retraining has acquired strategic importance for state policy. The article reveals an administrative-legal approach to regulating economic reintegration processes, which involves the creation of legal guarantees to ensure veterans’ access to the labor market, educational programs, financial resources and social services. The current regulatory and legal acts of Ukraine in the field of social protection and employment of veterans are analyzed, their strengths and weaknesses are identified. Attention is drawn to the fragmentation and insufficient effectiveness of existing mechanisms that complicate the process of employment, creating one’s own business and participation in support programs for small and medium-sized businesses. An important focus of the study is the study of international experience in the economic reintegration of veterans, in particular the practices of the USA, Canada, Israel and the countries of the European Union. It has been established that the most successful are models that combine legal guarantees, targeted social programs and partnership between the state, public organizations and the private sector. Based on this, proposals for Ukraine have been formulated, which include improving the system of vocational education and retraining, expanding state grant and credit programs, creating centers for supporting veteran entrepreneurship, as well as intensifying cooperation with international financial and donor organizations. It is concluded that effective economic reintegration of veterans is possible only under the condition of a comprehensive approach that combines administrative and legal instruments, modern economic mechanisms and public support. It is proposed to orient state policy towards the long-term perspective, taking into account not only the urgent needs of employment, but also the creation of conditions for the self-realization of veterans as active participants in the socio-economic development of the state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".