The Subject of Acceptance of an Offer, Promise or Receipt of Illegal Benefit in the Criminal Law of Ukraine
Bibliographic record
Abstract
The paper is devoted to issues on criminal and law characteristic of subjects of composition of a criminal offense that is provide by Article 368 of Criminal Code of Ukraine (acceptance of an offer, promise or receipt of illegal benefit by an official), and to analysis of current legislation in this field. Particular aspects of forming of legal regulation of criminal liability of officials for committing a criminal offense related to accepting an offer, promise or receiving an illegal benefit are researched. The issue of expanding the range of subjects of a criminal offense under Article 368 of the Criminal Code of Ukraine is analyzed. Experience of Criminal and Law Regulation of liability of certain categories of officials according to legislations of Austria, Norway, and Holland, Canada is analyzed. There is stressed that the analysis of the subjects of the crime under Article 368 of the Criminal Code of Ukraine and given in Note 1 to this Article allows to propose the text of the list included in Note 1 of Article 368 of the Criminal Code of Ukraine in the following wording, adding to the list of officials «Commissioner of the President of Ukraine for the Rights of the Child, Commissioner of the President of Ukraine for the Rights of Persons with Disabilities, Business Ombudsman, Chairman of the Accounting Chamber»; «Officials, in Articles 368, 3685, 369 and 382 of this Code are: 1) the President of Ukraine, … People’s Deputies of Ukraine, the Commissioner for Human Rights of the Verkhovna Rada of Ukraine, the Presidential Commissioner for Children’s Rights, the Presidential Commissioner for the Rights of Persons with Disabilities, the CMU Business Ombudsman, Chairman of the Accounting Chamber, Advisers and Assistants to the President of Ukraine, Chairman of the Verkhovna Rada of Ukraine, Prime Minister of Ukraine». There is outlined that current legislation enshrines the «duality» of the legal status of a Civil Servant. It is expressed in existence of Public Servants as a subject of Public Law, and as a private person — a subject of Private Law relations outside the performance of statutory obligations and rights. Obviously, that this legal «duality» influences on the character of Public Relations which is a subject of basic research in public administration, legal deontology, administrative and criminal law.
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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 | 0.002 | 0.004 |
| 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 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".