"ANALYSIS OF THE EXPERIENCE OF SOME FOREIGN COUNTRIES IN VICTIMOLOGICAL PREVENTION OF PREDMITTED KILLING "
Bibliographic record
Abstract
"The article examines the experience of some foreign countries in organizing the victimological prevention of intentional crimes related to murder. In many countries of the world, the protection of individuals from encroachments on their lives is the main area of prevention of premeditated murder. For this reason, it can be observed that victimological prevention, aimed at early identification of factors causing victims and their elimination, has become an urgent issue. The study used methods of analysis, logic, and comparative legal and statistical data analysis. The work carried out in a number of countries on the organization of victimological prevention of intentional homicide and the measures taken were studied and compared. In the conclusion, opinions were expressed on the advantages and prospects of implementing this experience in the conditions of Uzbekistan. The scope of work to be carried out to establish cooperation between law enforcement agencies and the population in organizing these preventive measures was also studied. A proposal was put forward to take into account the experience of such countries as the USA, Germany, Great Britain, Canada, and Russia in the organization of victimological prevention of premeditated murder, taking into account the aspects that correspond to the conditions of our country. "
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".