The Adversarial System in the Criminal Process of Ukraine: Technical and Legal Aspects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article substantiates the author’s scientific concept of electronic criminal proceedings, as regards the use thereof in the adversarial system, which would involve the formation of criminal proceedings as an electronic file, and the procedural interaction of the subjects of proceedings in an electronic law enforcement environment. The tasks of this article are as follows: analysis of issues that may arise when establishing such adversarial system in the criminal process of Ukraine; study of foreign experience of involving a defense lawyer in electronic criminal procedural processes; and development of proposals for improving the domestic practice of law enforcement. The Uniform Register of Pre-trial Investigations (URPI) has been defined as an electronic procedural document and an integral segment of criminal proceedings. The analysis of the electronic segment of the pre-trial investigation shows that the lawyer’s procedural status needs to be improved by his/her involvement in the URPI. Based on the analysis of the experience of electronic criminal proceedings in the province of Alberta (Canada), the Czech Republic, Sweden, and Kazakhstan, proposals have been drawn up to bring the defense to the URPI. As a result of the study, the author identified the legal and technical aspects of involving an attorney in electronic criminal proceedings, which suggested successive practical steps in creating personal virtual accounts, an algorithm for involving a defense lawyer in proceedings, and reforming the Uniform Register of Lawyers of Ukraine (URLU) as an electronic procedural legalization instrument.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it