Alternative Dispute Resolution vs. Judicial Conciliation in the Civil Process of Transit States: A Comparative Study
Bibliographic record
Abstract
The study investigates the current problems of judicial and extra-judicial conciliation procedures (alternative ways of resolving civil disputes) in states that have just started implementing such a procedural tool. Despite the fact that the term "conciliation procedures” is actively used in the science of civil procedure, this category is rather vague in countries that are just beginning to apply judicial conciliation in parallel with other conciliation procedures. Priority attention is focused on practical, legislative, and scientific problems of applying this procedural tool for rapid resolution of legal conflicts in Ukraine, as a state that only in 2017 (and in fact since the beginning of 2018) introduced this legal innovation. The purpose of the study is to elaborate on the legal nature and correlation between judicial conciliation (settlement of civil disputes with the participation of a judge under Ukrainian legislation) and alternative ways of resolving civil disputes. The study is based on several scientific methods that have identified the logic and general direction of knowledge of the problem of judicial conciliation. In particular, to determine the legal nature, essence, criteria of correlation, and delimitation of alternative dispute resolution and judicial conciliation, the study used the dialectical scientific cognition method. The study engages in a comparative study of the statutory regulation of similar procedures in the Russian Federation and Belarus legislation. It is concluded that alternative dispute resolution and judicial conciliation are closely interrelated and, depending on their types, can sometimes manifest themselves as synonymous categories or institutions of 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".