Methods of Pre-Trial Investigation of Criminal Offenses and Content of Its Structural Elements: Case of Kazakhstan
Bibliographic record
Abstract
Rationale for research is presented in the form of a proposed structure of the methodology’s elements for pre-trial investigation of criminal offenses. It is notable that this novelty is based on the provisions of the new edition of Criminal Code and Criminal Procedure Code of the Republic of Kazakhstan, which came into force on January 01, 2015. The research objective is to enhance the efficiency of investigating criminal offenses against the foundations of the constitutional order as well as the security of the state, criminal offenses in the field of informatization and communications, criminal offenses in the field of economic activity and medical criminal offenses, which, in turn, are the sources of the development of the structure of the methodology for pre-trial investigation of such types of socially dangerous acts. They were previously not covered in public editions. Research methods are the formation of the data structure of particular methods of pre-trial investigation of crimes based on not just theoretical experience, but also the accumulated practice of a preliminary investigation in the country, the new and far abroad, for example, crimes against the person, property, etc. Research results include the developed structure of the methodology for pre-trial investigation of these crimes. The study will serve as an impetus for further theoretical development of particular methods of pre-trial investigation of certain types of criminal offenses along with their implementation in the practice of pre-trial investigation in the country’s investigative apparatus.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".