Adaptive Case Management in the International Practice of Civil Proceedings
Bibliographic record
Abstract
The main problems of civil proceedings that need effective tools to address them in Ukraine are: (1) deadlines for civil cases, (2) ineffective regulation of various procedural stages and court proceedings, (3) insufficiently developed institutions and tools for judges to expedite consideration of a case in a separate case or effectively consider repeated cases.The purpose of the study is to develop scientifically sound proposals and recommendations for the implementation of the principles of Adaptive Case Management in civil litigation.The ACM (Adaptive Case Management) system has proposed as the newest tools that can ensure the adaptation of the civil justice system to the new operating conditions.The main element in the ACM approach is a case, which can include a large number of elements -people, events, documents, processes, discussions and more.Adaptability means that each case can be unique and adapted to the current situation.Using of digital tools in civil proceedings ensure the optimal ratio of activity of the parties and the court in the conduct of proceedings in civil cases, speed up processes, increase efficiency.Adaptive Case Management in civil litigation will ensure the optimal balance of activity between the parties and the court in litigation in civil cases.This innovation will improve the organization of proceedings in civil proceedings, which will increase the efficiency of justice and the effectiveness of civil proceedings and will be the basis for a conceptual rethinking of the role and function of judges and parties in the proceedings.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".