The Peculiarities of Conducting Special Operational-Search Measures in the Fight Against Crime
Bibliographic record
Abstract
The relevance of the study is due to the need to determine the feasibility of using special operational-search measures in the system of operational-search activities of the authorized bodies of Kazakhstan and to establish their place and importance in the fight against crime. In this regard, this article is aimed at identifying and disclosing the essence of operational search activities, identifying their main content. A comparative study of the legislation of individual countries providing for similar activities was carried out in order to identify the features of the conduct and the legal regulation of operational search activities and their significance. As a result of the study, it was concluded that the features of special operational-search measures are manifested only in their number, name, and partly in the content of the actions taken, while their essence is manifested almost equally. Along with this, to ensure the reliability of the results obtained, scientific and technical means are being actively introduced, mechanisms and methods for recording operational information are being improved, and scientific knowledge is being accumulated and well-grounded recommendations for conducting operational-search measures. The materials of the article are of practical value for the bodies that carry out operational investigative activities, scientific and practical workers of the authorized bodies.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".