The Current Situation Concerning Investigative and Operational Search Powers of a Crime Investigator in Criminal Proceedings in the Former Soviet Union
Bibliographic record
Abstract
This article studies legal situation concerning the use of operational search activity results in criminal trial and, namely, the procedure of execution of crime investigator’s investigative and operational search powers within the framework of criminal procedure legislation of the former Soviet Union (in the case of Ukraine, Georgia, Estonia, Latvia, Lithuania, Moldova, Russia, Belarus and Kazakhstan). This study will allow gaining greater insight into the essence and prospects of further development of criminal proceeding in the context of modernization of the criminal justice system and its bringing to conformity with the international standards in the Republic of Kazakhstan. On January, 1, 2015 the new Criminal Procedure Code of the Republic of Kazakhstan is put into force. One of its key innovations is Chapter 30 regulating undisclosed investigative activities. Thus, this article studies the legal situation concerning the use of operational search activity results which according to the new Criminal Procedure Code of the Republic of Kazakhstan represent undisclosed investigative activities. On the basis of the study carried out the author has found out certain problems to be solved in the short term, has developed his own viewpoint and offered certain proposals concerning some points at issue.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".