Compliance of the Criminal Legislation of Ukraine with the UN Protocol on Firearms
Bibliographic record
Abstract
Problems with regulating the trafficking of weapons and ammunition emerged in Ukraine after the collapse of the Soviet Union. The Soviet military left behind a large number of small arms, light weapons and ammunition, much of which did not have proper (in some cases any whatsoever) inventory control and accounting. The purpose of the study is to find the best models of criminal liability for illicit manufacturing and trafficking of firearms in Ukraine, its parts and components and ammunition, taking into account international experience. The study investigates the issue of compliance of the criminal legislation of Ukraine with the United Nations Protocol against the Illicit Manufacturing of and Trafficking in Firearms, Their Parts and Components and Ammunition, supplementing the United Nations Convention against Transnational Organised Crime, and proposes for harmonisation of national legislation with this Protocol. It is concluded that Ukraine needs to adopt the Law "On Arms Trafficking", which, among other things, must clearly define such concepts as "firearms", "parts and components", "ammunition", "illicit manufacturing", "illicit trafficking", etc. which would also be relevant for the criminal legislation of Ukraine.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".