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Problems and prospects of economic reintegration of war veterans: an administrative-legal approach

2025· article· uk· W4416226342 on OpenAlexaboutno aff
Serhii Sivkov

Bibliographic record

VenueAnalytical and Comparative Jurisprudence · 2025
Typearticle
Languageuk
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Strengths and weaknessesUkrainianGeneral partnershipRetrainingState (computer science)Social PartnershipSocial protectionEconomic stability

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0070.009
Scholarly communication0.0100.005
Open science0.0020.006
Research integrity0.0020.003
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 head0.080
GPT teacher head0.319
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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