Prolonged Experience in Combating Economic and Organized Crime in Ukraine (1999-2022)
Bibliographic record
Abstract
In the research papers on victimology the academicians usually define victimization as the whole complex of all cases when an individual (a social community) suffers moral or bodily injury and damage in a crime. Under the cited above meaning “victimization” as the gene-realization of the whole vicinity realized is the most appropriate term that corresponds to the term “crime”. In a certain extent victimization is simultaneously the measure of human destructibility realized in crimes. At that business victimization levels exceed the levels of crimes related to them one and a half time. At the same time the level of residential victimization totally tops the number of crimes committed against businessmen in 2 and 2,5 times. Thus the breach is the more, the better is the public’s activity in combating crime and the less is its reliance in private security. Thus businessmen’s anxiety about property security and extra emergency measures undertaken by them equalize the levels of victimization and crime. On the other hand, equalization of the victimization and crime levels is caused by the process of objective coalescence of business units and criminal groups and the pronounced tendencies to restricting influence of organized crime upon businessmen.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".