Узагальнення кращих практик зарубіжних держав у сфері протидії корупції для України
Bibliographic record
Abstract
The article provides a comprehensive analysis of the most successful foreign anti-corruption practices developed in countries such as Singapore, Georgia, Romania, Sweden, South Korea, Poland, the Czech Republic, France, Canada and the United States. Particular attention is paid to institutional models and mechanisms for coordinating anti-corruption policy. The role of specialised anti-corruption bodies, systems for monitoring the activities of civil servants, the digitisation of administrative procedures and tools for ensuring transparency are analysed. The examples of Singapore and South Korea demonstrate the effective combination of strict anti-corruption oversight with high standards of public administration. The experience of Georgia and Romania has shown the effectiveness of rapid institutional reforms with international support and active public control. The Swedish model, on the other hand, demonstrates a long-term preventive effect thanks to deep-rooted political integrity and openness in public administration. The comparative analysis identifies the most effective anti-corruption tools that have the potential to be adapted to the Ukrainian context. The feasibility of introducing the principle of inevitability of responsibility, improving financial control mechanisms, strengthening the institutional capacity of anti-corruption agencies, introducing modern IT solutions in public administration, and ensuring the independence of investigative and judicial bodies has been substantiated. The critical importance of political will at all levels of government and the active participation of civil society has been emphasised. Specific steps are proposed for the implementation of relevant mechanisms in the legislation and practice of Ukraine’s anti-corruption policy, taking into account the challenges associated with the state of war and post-war reconstruction. Particular attention is paid to analysing the challenges associated with transferring foreign experience to the Ukrainian legal space. The risks of mechanically copying models without taking into account the socio-cultural context are considered. The importance of adaptation is emphasised, taking into account national specifics, institutional capacity and the level of trust in state institutions. The article may be of interest to researchers, lawyers, politicians and experts in the field of public administration who are involved in reforming anti-corruption policy and developing effective models for preventing abuse in the public sphere.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".