International legal mechanisms for preventing corruption: Analysis of effectiveness and prospects for use in developing the anti-corruption architecture of Ukraine
Bibliographic record
Abstract
The purpose of the paper was to analyse international experience in combating corruption offences for its further integration into Ukrainian legislation. The study used such methods as system analysis, comparative-implementation, statistical and retrospective methods. The article conducted a systematic review of global concepts of corruption prevention. It is indicated that in the conditions of modern globalisation processes, borrowing successful foreign experience and implementing it into the current legislation of Ukraine is critically important. A number of preventive measures used by leading countries to prevent corruption offences are also analysed in detail. Particular attention is paid to the positive experience of countries with the lowest level of corruption, and ways to achieve such results are highlighted. The article considers the anti-corruption strategies of Singapore, South Korea, Finland, Sweden, the Netherlands, Belgium, the Slovak Republic, Israel, the Republic of Poland, Germany, Great Britain, Denmark, the United States of America, Canada, Romania, Estonia. It is pointed out that in countries with low levels of corruption, prevention models combine both repressive measures and comprehensive elimination of factors contributing to corruption. It is noted that a modern strategy for preventing corruption requires active cooperation between state bodies, law enforcement agencies and civil society in matters of prevention and combating corruption offences. An essential prerequisite for success in preventing corruption is also the growth of civil awareness. Given the European vector of Ukraine’s development, there is an urgent need to develop and implement a modern anti-corruption policy. It should take into account positive international experience in this area
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".