Bibliographic record
Abstract
The article examines the issue of the overloading of Ukraine’s judicial system, which negatively affects the efficiency of justice and public trust in judicial institutions. The main causes of this phenomenon are analyzed, including an insufficient number of judges, gaps in legislation and procedural mechanisms, and the lack of effective alternative dispute resolution methods such as mediation and arbitration. The shortage of judges, combined with an increasing number of cases, leads to excessive workloads for the judiciary, which in turn reduces the quality of judicial decisions and extends case processing times. Legislative gaps and the complexity of procedural mechanisms create conditions for delays in court proceedings and abuses of procedural rights. The absence of a well-developed system of alternative dispute resolution methods forces citizens to turn to courts even in cases that could have been resolved through extrajudicial means. The consequences of court overload include a decline in the quality of judicial decisions, violations of the right to a fair trial within a reasonable timeframe, growing public distrust in the judicial system, as well as economic and social losses associated with ineffective legal protection. A comprehensive approach to solving the problem is proposed, which includes expanding the number of judges and ensuring their proper training, optimizing procedural legislation to reduce case processing times, developing and promoting alternative dispute resolution methods, and implementing electronic courts and other digital solutions to simplify judicial proceedings. The article also examines the experience of other countries, particularly European states, the United States, and Canada, in addressing court overload. It is noted that the use of digital technologies in judicial proceedings and the introduction of alternative dispute resolution methods contribute to increasing the efficiency of the judicial system and reducing the burden on courts. The implementation of the proposed reforms requires close cooperation between government agencies, civil society, and international partners. Only through joint efforts can the efficiency of Ukraine’s judicial system be improved and public trust in justice restored.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".