The Adversarial System in the Criminal Process of Ukraine: Technical and Legal Aspects
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.
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 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 | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".