Some Legal Aspects of the Circulation and Research of Narcotic Drugs and Precursors
Bibliographic record
Abstract
An analysis of criminal law practice suggests that when considering criminal proceedings of various categories (homicide, rape, theft, production, acquisition, storage, transportation and sale of drugs, etc.), DOI: 10.32370/IA_2020_09_6 material evidence is often represented by narcotic drugs of both illicit manufacturing and those produced by the pharmaceutical industry.Narcotic drugs are a group of pharmacologically active substances of plant and synthetic origin that can selectively affect the central nervous system, resulting in the complete loss of consciousness, loss of all types of sensation and relaxation of skeletal muscles (anaesthesia), or a special psychological and physiological state of the human organism, at which the absence of the habitual poison (drug) causes a number of disagreeable sensations requiring periodic drug use [1].This reaction is explained by the fact that drugs are quickly included in the metabolism and become indispensable to life.Meanwhile, they are not called poison for anything, since they lead to the physical weakening of the organism, and then to inevitable death.Drug use causes the development of drug addiction (from the Greek narkesleep, numbness and maniapassion, mania) is clearly recognized morbid impulse and addiction to drugs that produce pleasant sedation or agitation (and some drugsalso illusions and hallucinations) followed by the central nervous system depression.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".