On Some Issues Concerning Section 4.5. “Crimes Against Human Willand Dignity” of the Draft Criminal Code of Ukraine
Bibliographic record
Abstract
The socio-political and economic situation in the state determines the improvement of the national legal system, considering modern challenges and needs of a democratic society. The Criminal Code of Ukraine, the draft of which is currently under development, is no exception. The purpose of this study is to analyse approaches to the proposed wording of articles of the draft Criminal Code of Ukraine concerning liability for crimes against human will and dignity, identify shortcomings and determine ways to eliminate the latter. To fulfill this purpose, the authors employed the following methods: logical-semantic, comparative, hermeneutical, dogmatic (Aristotelian) and system analysis. The study uses current Ukrainian and foreign legislation, works of researchers from Canada, United Kingdom, Switzerland, Uganda, Ukraine, and the USA, as well as materials of judicial practice. The study proves that Section 4.5. “Crimes Against Human Will and Dignity” of the draft Criminal Code of Ukraine protects the right to freedom and personal integrity. The authors establish that the term “freedom” better corresponds to the object of encroachment and thus is more appropriate. This study offers original definitions of the terms “right to freedom” and “right to personal integrity”. It is argued that the term “representative of a foreign state” in Article 4.5.1. “Meaning of the terms used in this Section” is worded inaccurately. Comments are made on the wording of Article 4.5.4. “Illegal deprivation of person's liberty” concerning the debatable nature of combining acts differing in terms of the primary direct object in one norm – illegal detention, compulsory attendance, house arrest or detention, illegal placement in a psychiatric care institution, kidnapping and illegal deprivation of liberty; the study provides an author's version of Paragraph “b”, Article 4.5.4. The practical value of this study is to highlight scientific opinions on some matters concerning the regulation of crimes against the person's will and dignity in the draft Criminal Code of Ukraine and formulate proposals in this regard, which can be considered by the developer of this project – the Working Group on criminal law development
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".