Online courts and Online Dispute Resolution in terms of the international standard of access to justice: international experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it