Противодействие коррупции как цель правоохранительной политики: отечественный и зарубежный опыт
Bibliographic record
Abstract
The phenomenon of corruption is inherent to society at all stages of its development, as well as various segments of public life and has enough deep historical roots, due to social, economic and political factors, as well as characterized by specific national and state forms of embodiment. Corruption combined with the lack of professionalism of officials is one of the main causes of social and economic crises. The problem of anti-corruption has never lost its relevance. A special place is given to the implementation of law enforcement policy that is associated with the use of state coercion measures against offenders, enforcement of the imposed measures (sanctions), as well as the implementation of measures aimed at preventing crime in the future, in this case of corruption crimes. The purpose of research is to explore the domestic and international experience in the implementation of judicial policy in countering corruption. To achieve this goal solves problems disclosure concepts of law enforcement policies and anti-corruption measures are analyzed in the foreign and domestic level. The study of law enforcement policy in combating crimes of corruption is based on an analysis of the causes and conditions for the development of corruption crimes. Feature of the study is that analyzes the wealth of experience available in the international community on combating corruption. The content of anti-corruption strategy as a set of legal, political and organizational measures aimed at: 1) the formation of the anti-corruption mood of society; 2) to raise awareness about the dangers of corruption; 3) international qualification standards corruption offenses, harmonization of jurisdictional parameters; 4) creation of transparency of the atmosphere, primarily in the financial sector to enable the identification of acts of corruption; 5) principle of inevitability of punishment; 6) ensuring fair compensation to victims of crimes of corruption factors, is analyzed. The anti-corruption experience of countries such as Finland, Denmark, New Zealand, Iceland, Singapore, Sweden, Canada, the Netherlands, Luxembourg, Norway, Australia, Switzerland, United Kingdom, Hong Kong, Austria, Israel, USA, Chile, Ireland, Germany, Japan is disclosed. Importance of the study of law enforcement policy in fighting corruption is the need for timely and effective prevention programs aimed at preventing crimes of corruption.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".