Foreign experience of public administration in emergency situations
Bibliographic record
Abstract
The article examines international practices in public administration during emergencies, with a focus on adapting them to Ukraine’s specific realities. The relevance of this topic is highlighted in the context of increasing natural, technological, and humanitarian crises, which demand effective performance from public institutions. Special attention is given to the challenges posed by Russia’s full-scale aggression against Ukraine, accompanied by widespread destruction and socio-economic crises. The article reviews best practices in public administration during emergencies implemented in countries such as the United States, Germany, Japan, the United Kingdom, Israel, Canada, and Australia. Key elements of their crisis management models are identified, including: a multi-level institutional structure with clearly defined powers; legal frameworks for crisis management based on overarching laws and action plans; advanced communication strategies to ensure timely public information dissemination; the use of innovative technologies such as geographic information systems, artificial intelligence, and drones; and active collaboration between government agencies, local authorities, civil society organizations, and the private sector. The analysis concludes that effective crisis management integrates centralized strategic leadership with broad autonomy for regional and local authorities. Particular emphasis is placed on Israel’s experience, especially its strategic communication models, the involvement of volunteer organizations, and the practice of preparing citizens for military-related emergencies. The necessity of implementing a national early warning system, developing situational centers, and digitizing management processes in Ukraine, particularly for emergency forecasting, is underscored. Specific recommendations are provided for improving the legislative framework, enhancing institutional capacity, adopting modern technologies, and developing effective communication strategies. The paper outlines prospects for further research aimed at incorporating international experience into Ukraine’s legal and administrative framework and formulating practical recommendations to optimize the functioning of public authorities and local governments during crises.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".