Online courts and Online Dispute Resolution in terms of the international standard of access to justice: international experience
Bibliographic record
Abstract
The article is devoted to the analysis of the problem issues of the Online Dispute Resolution (ODR) through the prism of international standard of access to justice in civil matters. The first part of the article refers to terminological inconsistency, which is connected with using of three synonyms refering to IT-technologies in the area of civil justice, in particular cyberjustice, digital justice and e-justice. The author proposes to use term “e-justice”, which involves e-filing, electronic systems of assignment of cases, e-case-management, eDiscovery, ODR, electronic systems of court practice, using of Artificial Intelligence in civil proceedings. In the second part of the article the narrow and wide approach to the ODR are described. According to narrow approach ODR is described as online ADR. Wide approach to ODR includes online ADR as well as online courts. Today wide approach is more valid taking into account recent developments in the field of online courts in foreign countries. The third part of the article describes different types of online courts, in particular, online Civil Resolution Tribunal (British Columbia, Canada), Online Solutions Court (Great Britain) etc. The author analyzes current innovations in the structure of online courts, connected with integration of information systems and online ADR into the online courts platforms. Special attention is paid to the use of Artificial Legal Intelligence in courts with references to advantages and challenges of such innovations.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".