Anti-Corruption Expertise OF Draft Legislation: the Experience OF Some European Countries AND Canada
Bibliographic record
Abstract
This paper examines the existing mechanisms for detecting and eliminating corruptogenic norms in some European countries, as well as Canada. Examples of the use of the well-known term “anti-corruption expertise of draft legislation”, as well as the lack of its application, which does not exclude the use of anti-corruption expertise mechanisms in the framework of other procedures of the rule-making process, are described. The author considers the positive experience of some countries (Moldova, Lithuania and Canada), the norms of which are proposed to be implemented in national legislation. In particular, the multilevel systems of "filtering" of developed projects, in which both state organizations and the non-state sector take part, deserve special attention. Also, a positive experience is the well-established system of joint work and close interaction between state bodies authorized to conduct anti-corruption expertise of draft regulations, with public organizations representing the interests of the population of certain territories or other persons. At the same time, the opinions of foreign experts are given regarding certain issues in the field of anti-corruption expertise, as well as on the fundamental issues of applying the concept of "anti-corruption expertise of draft legislation" in general. At the same time, the opinion of the author regarding the studied topics and the presented work is given.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".