Approaches to transnational organized crime. Prevention and combat by international organizations
Bibliographic record
Abstract
A feature of the contemporary erass, determined by technical and scientific progress, by the intense exchange of material and spiritual values, is the tendency to intensify cooperation between States on various levels of political, social, cultural and legal activity. International crime is not a figment of people with a rich imagination, it is a reality that threatens to turn our lives into a nightmare with bad consequences. At the moment, all countries of the world, regardless of their level of development, are facing a real explosion of international crime. Recognizing the extent and special danger of international crime, the problem of preventing and countering it began to concern the suprastatal forums, which, in stilling its expansion and evolution, put emphasis on regulating some forms of its activity, through the conclusion of regional agreements and international conventions. Thus, the eradication of crime is a primary objective for both developing and developed countries. It should be noted that criminal organizations in Moldova have multiple links with similar structures in CIS countries, Romania, Poland, Germany, Italy, the USA, Canada, Israel. Despite the imperfection of legislative amendments and additions, we consider, however, that a legislative basis in countering international crime in Moldova exists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".