Interaction between state power and local self-government in Ukraine: problems of formation and directions of improvement
Bibliographic record
Abstract
In recent decades, significant changes have been taking place in the governance system in Ukraine, aimed at increasing the efficiency of government bodies and strengthening the role of local communities. The article thoroughly examines the historical evolution of interaction between state authorities and local self-government in Ukraine, paying special attention to modern decentralization processes that began after the Revolution of Dignity in 2014. Decentralization has become one of the key reforms aimed at modernizing the governance system, optimizing the distribution of resources and powers, and strengthening the role of local communities. This is a major step towards democratic development and modernization of public administration. However, the analysis shows that the implementation of this reform is accompanied by numerous problems. To find effective solutions to the identified problems, a comparative analysis of the Ukrainian and Canadian experiences is carried out. Canada, with a long history of local self-government, has become an example of an effective decentralized management model. The Ukrainian experience of decentralization, despite certain achievements, remains at the formation stage. Significant successes include the creation of united territorial communities (UTCs), the transfer of financial resources to the local level, and the improvement of infrastructure in certain regions. At the same time, the identified shortcomings indicate the need to improve the legislative framework, increase the transparency of resource allocation, and increase citizen involvement in management processes. To enhance local self-government, we need to strengthen local capacities and foster partnerships between central and local authorities to improve resource management and public service quality. Modern decentralization in Ukraine is a revival of local self-government, adapted to contemporary needs and drawing on historical and international experience. It aims to create a more agile and efficient management system capable of addressing current challenges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".