Human Resource Management in Public Administration: content, current trends and challenges in the context of Ukraine
Bibliographic record
Abstract
This article presents a comprehensive analysis of the current state of Ukraine’s public personnel policy in the context of ongoing globalization, digital transformation of public administration, and the exceptional circumstances of martial law. The study examines the theoretical and methodological foundations of personnel policy, its structural and functional dimensions, as well as the institutional and legal limitations that significantly affect the effectiveness of its implementation across all levels of public service. It is emphasized that personnel policy in Ukraine remains fragmented, normatively inconsistent, and highly susceptible to political fluctuations, which hinders the establishment of a stable, professional, and ethically oriented system of public administration. Special attention is given to the analysis of international experiences in implementing HRM strategies in EU countries, the United States, Canada, Germany, and the Nordic region. A comparative assessment of career-based and position-based civil service models is provided, along with an overview of modern trends in public HRM, including digitalization, ethical leadership development, strategic HRM practices, and human-centered governance. Based on this analysis, the article proposes key strategic directions for the modernization of Ukraine’s personnel policy. The conclusions emphasize the need to shift toward a new paradigm of personnel policy - one that prioritizes professionalism, accountability, ethical standards, and institutional resilience as cornerstones of sustainable public administration.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".