Повоєнна модернізація публічної служби: від викликів до інституційних змін
Bibliographic record
Abstract
The article is devoted to the study of the process of post-war modernization of Ukraine’s public service as a necessary condition for the effective functioning of the state under the conditions of post-war recovery. The armed aggression of the Russian Federation against Ukraine has triggered profound transformations in the political, social and administrative environments, creating new challenges for the public governance system. Particular attention is given to analyzing the challenges faced by the public service resulting from decentralization, shifts in the state policy priorities, the need to ensure institutional resilience, as well as increasing citizen expectations regarding transparency and the effectiveness of governmental decisions. The article substantiates the relevance of shifting from the traditional model of civil service to one that aligns with the principles of resilience governance – governance that is stable, adaptive and innovative. It outlines the need to develop human resource potential, strengthen institutional capacity, implement digital transformation of management processes and promote the principles of openness, integrity, operational flexibility and responsiveness. The author put an emphasis on the importance of fostering a culture of continuous improvement and strategic foresight within public institutions. The study explores examples of international experience in post-conflict civil service recovery, with particular emphasis on Canadian practices, which illustrate the importance of policy coherence, interagency coordination and sustained investment in human capital. Key vectors of institutional change are identified, including the revision of the regulatory framework, the transformation of managerial approaches and others. It was concluded that the post-war modernization of the civil service should be implemented in a systemic and integrated manner, ensuring a well-calibrated balance between flexibility and stability, accountability and innovation, institutional memory and openness to change. Such an approach will not only strengthen the resilience of public institutions, but also foster public trust and support Ukraine’s long-term democratic development.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".