Criminal Prosecution for Organizing or Maintaining Dens for the Consumption of Narcotic Drugs, Psychotropic Substances, Their Analogues and Providing Premises for The Same Purposes Under the Legislation of the Republic of Kazakhstan
Bibliographic record
Abstract
The main aim of the study is to consider problematic issues related to the qualification of a criminal offense, provided for in Article 302 of the Criminal Code of the Republic of Kazakhstan “Organization or maintenance of dens for the consumption of narcotic drugs, psychotropic substances, their analogues and the provision of premises for the same purposes”. This article describes the circumstances that make it difficult to prosecute persons who provide their homes or other premises for the consumption of narcotic drugs, psychotropic substances, their analogues, as well as the organizers of this type of illegal activity, creating conditions for anesthesia of the population. Through the study and analysis of statistical information, available approaches to this issue, as well as materials of judicial investigative practice in cases of this category, an attempt was made to consider the causes and conditions conducive to the commission of this offense, an author’s vision of resolving the situation was proposed. The article presents the data of criminal statistics in Kazakhstan for 2015-2018, identifies the most typical conditions conducive to the creation and functioning of drug traffickers, makes reasonable proposals for amending Article 302 of the Criminal Code of the Republic of Kazakhstan, aimed at improving the current criminal law. The materials of the article can be of practical value for law enforcement officers fighting the specified type of crime.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".