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Record W1517018113 · doi:10.5539/res.v7n7p187

Combat Against Trafficking in Narcotic Drugs, Psychotropic Substances and Precursors in Kazakhstan

2015· article· en· W1517018113 on OpenAlexvenueno aff
Svetlana Baimoldina, Balausa Amanovna Berdimbetovna, Shynarbek Kairbekuly Akchabaiev

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsnot available
FundersLomonosov Moscow State University
KeywordsNarcotic drugsLegislationIllicit drugNarcoticCriminologyPolitical scienceNarcotic analgesicsDrug traffickingDrugLawPharmacologyMedicinePsychiatryPsychology

Abstract

fetched live from OpenAlex

The article describes the theoretical, legal, and criminological issues of international cooperation of Kazakhstan, national models of some countries in the combat against drug trafficking, and the spread of drug use. The authors disclosed the basic concepts of structural elements of the mechanism for legal regulation of the combat against illicit trafficking in narcotic drugs, psychotropic substances and precursors in Kazakhstan, carried out the analysis of modern criminal and common law in terms of regulation of the methods of combating against illicit trafficking in narcotic drugs, psychotropic substances and precursors and related compounds of these types of crime, criminological characteristics of drug-related crimes across regions and areas of Kazakhstan. The result of the scientific research is the formulated theoretical propositions and recommendations for improving the current legislation of the Republic of Kazakhstan and its practical application.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.121
GPT teacher head0.399
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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