КОМУНІКАЦІЯ ПРИ ПРАЦЕВЛАШТУВАННІ ВЕТЕРАНІВ НА ПУБЛІЧНУ СЛУЖБУ: ПІДТРИМКА ВЕТЕРАНІВ В УМОВАХ РЕФОРМИ ДЕРЖАВНОГО УПРАВЛІННЯ ТА ЄВРОПЕЙСЬКОЇ ІНТЕГРАЦІЇ
Bibliographic record
Abstract
The article examines how government communication can serve as a practical public policy tool to increase veterans’ participation and improve their performance in competitive recruitment for public service positions without undermining competition, meritocracy, equal access, or non-discrimination. The relevance of the study is driven by the growing number of veterans after 2022 and the need for their effective professional reintegration alongside the restoration and modernization of civil service recruitment within Ukraine’s public administration reform and EU integration agenda. In this context, the quality of government communication determines whether recruitment rules are clear to applicants, applied consistently across institutions, and capable of appropriately interpreting military experience within civilian competency frameworks. The theoretical and methodological basis of the study combines the principles of public administration as a benchmark for a professional civil service; policy analysis, which treats communication as a policy instrument with measurable outcomes; and a network governance approach that highlights coordinated interaction among the Ministry for Veterans Affairs of Ukraine, the National Agency of Ukraine on Civil Service, HR units, local authorities, civil society organizations, and international partners. The empirical analysis draws on a comparison of practices in the United States, the United Kingdom, and Canada, focusing not on preferential treatment itself but on how communication reduces information barriers, standardizes rules, and supports transparent implementation of existing procedures. The findings show that in the United States effectiveness is achieved through standardized guidance, clear instructions, and consistent interpretation of procedures, which reduce technical application errors and increase predictability for candidates and HR professionals. In the United Kingdom, the guaranteed interview scheme for veterans who meet minimum requirements lowers the initial access barrier while preserving competency-based selection. The Canadian experience highlights the importance of data reliability and institutional oversight, as fairness in recruitment depends on accurate status verification, clear explanations of procedures, and transparent responses to technical issues. For Ukraine, the article proposes a coordinated government communication model for veterans’ access to public service, including joint communication approaches by the Ministry for Veterans Affairs and the civil service authority; unified wording in vacancy announcements and explanations; a pilot guaranteed interview mechanism as a communication entry point to competition for minimally qualified veterans; a tool to translate military skills into civilian competencies for applicants and HR; regular publication of clear performance indicators; and training for HR staff on ethical and unbiased assessment of combat and service experience without stigmatization. The practical value of the study lies in strengthening trust in competitive recruitment, reducing information asymmetry for veterans, and supporting public administration reform through transparent and merit-based procedures aligned with EU integration requirements.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".