STATE REGULATION OF THE NATIONAL ECONOMY: WORLD EXPERIENCE AGAINST CORRUPTION
Bibliographic record
Abstract
The article examines the features of anti-corruption in foreign countries. In particular, the concept of corruption in international acts is defined, as well as the peculiarities of the legal regulation of anticorruption in Germany, Israel and Esto-nia. It is recommended to use the experience of these countries to combat corruption in Ukraine. It is noted that in general, the mechanisms for combating corruption in foreign countries are not fundamentally different. The main difference is in the ap-proaches and motivation of their application. Therefore, in order to successfully overcome corruption, it is necessary to have not only perfect legislation, but high-quality work of anti-corruption bodies. It has been shown that an analysis of inter-national legal instruments defining corruption suggests that corruption at the inter-national level is interpreted as an abuse of power or a notion of trust for the sake of personal privileges or in favor of privileges of another person or group to whom loyalty is observed. The analysis concluded that the countries that have created an effective anti-corruption mechanism include: Germany, Finland, Denmark, New Zealand, Iceland, Singapore, Sweden, Canada, the Netherlands, Luxembourg, Norway, Australia, Switzerland, Great Britain , Austria, Israel, USA, Chile, Ireland and others
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".