ON IMPROVING THE LEGISLATIVE REGULATION OF FALSIFICATION OF EVIDENCE AND INTELLIGENCE, COUNTER-INTELLIGENCE MATERIAL
Bibliographic record
Abstract
This study focuses on the analysis of Article 416 of the Criminal Code of the Republic of Kazakhstan on falsification of evidence and operational-search, counter-intelligence materials on the fact that this article has insufficient definition and does not cover all illegal actions with evidence and operational-search, counter-intelligence materials. Actions such as destruction, concealment and seizure are not less dangerous than falsification. However, these actions have not been criminalised yet. While there are examples of foreign states (Latvia, Georgia, Uzbekistan, Canada, Sweden, India), which enshrine in their criminal legislation different ways of committing illegal actions in respect of evidence, such positive experience should not be overlooked. Under part 4 of Article 416 of the Criminal Code of the Republic of Kazakhstan there is a certain inconsistency with the criminal procedural legislation in terms of establishing a narrow range of persons in the disposition of this norm who are liable for falsification of evidence in criminal proceedings. This creates problems in criminal law assessment of unlawful actions with evidence and limits possibilities of the criminal law to bring to responsibility for such actions a wide range of persons who simply are not listed in the disposition of clause 416 part 4 of the Criminal Code of the Republic of Kazakhstan. The mentioned problems reduce efficiency of prevention of falsification of evidence and operative-search, counter-intelligence materials as a highly latent crime. In order to eliminate this, amendments to the current version of article 416 of the Criminal Code of the Republic of Kazakhstan are proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".