Bibliographic record
Abstract
The article is devoted to the analysis of the current state of the jury in Ukraine, assessment of existing problems in legislative regulation, ambiguity of enforcement and finding possible ways of solving existing problems. The jury's ability to defend the judicial system against some tendencies that take place in the trial corps and can undermine a fair system of criminal liability and punishment is argued. A statistical analysis of criminal and civil cases involving jury in 2017-2018 is provided. The article emphasizes that the successful functioning of the jury depends first and foremost on the procedure of selection of people for jury. The bill drafts of Ukraine of amendments to the current legislation on improvement of the procedure of forming the list of jury are analyzed. In the context of this, attention was paid to the issues of the quantitative composition of the jury, the transfer of power to form a list of jury to State Judicial Administration of Ukraine, the heterogeneity of the composition of the jury in terms of individual and psychological differences. The expediency of working out the Regulation “On the procedure of selection of citizens in the jury”, where all requirements for candidates for jury will be detailed, restrictions for people to be sworn, the procedure of forming the lists of jury and their submission to court, is stated. The outreach campaign and legal education of the population is аn important element of the effectiveness of the jury. The requirement of continuity of the trial in the case of jury which is related to the concentration of their attention and various kinds of influence, in particular the mass media, is emphasized. It is noted that the review of the decisions of the jury in the appeal court by professional judges, in fact, negates the value of the verdict of the jury.The item of allotments to candidates for jury and the experience of foreign countries on this issue are considered. It also substantiates the demand of transition of Ukraine to the classic jury trial model, which successfully operates in such developed countries as the United States, Canada, the United Kingdom, Denmark, Belgium and others. The introduction of a classic jury trial model is absolutely necessary in the current condition, will increase the public's confidence in the judicial system and will introduce an effective competitive model of criminal proceedings. Key words:jury, verdict of the jury, selection of the jury.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".