Selection of Jurors and Lay Assessors in Comparative Perspective: Eurasian Context
Bibliographic record
Abstract
This article compares (1) the qualification of jurors or lay assessors; (2) methods of listing candidates for lay adjudication; and (3) selection and empanelment of jurors and lay assessors for a particular case, in various post-Soviet countries and Western countries. Two key issues are examined. The article examines whether the legislation of post-Soviet countries in relation to the qualification, listing and empanelling of jurors and lay assessors is consistent with the standards applied in developed democracies. Simultaneously, the article explores what standards and rules of selection of lay adjudicators should be incorporated into the legislation of post-Soviet states in order to insure impartiality and independence of lay adjudicators. The article reveals a significant number of defects and gaps that allow executives and court personnel to manipulate the selection process and hamper the formation of impartial, independent and representative lay courts. An examination of the legislation in post-Soviet countries and of the empirical data collected in Russia lead to the conclusion that the mechanisms of the voir dire, peremptory challenges and challenges to entire juries should be reviewed and improved in order to provide reliable safeguards for the selection of impartial and independent lay adjudicators and prevent parties from excluding prospective lay adjudicators for discriminatory reasons.
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".