Judicial dispute resolution (JDR) new roles for judges in ensuring justice
Bibliographic record
Abstract
This book describes the ways in which judges, using JDR, have been facilitating problem-solving among litigants, and in the process, ensuring more just outcomes. JDR or judicial dispute resolution is similar to mediation (or alternative dispute resolution - ADR, as it is sometimes called), but it is provided by a judge, not a private mediator. Very little has been written about JDR, especially in Canada where it has been pioneered for several decades, because all the records have remained confidential. The story can now be told because the authors were given exclusive access to the records and the parties (including the JDR judges) in nine illustrative cases. The authors provide a complete Teaching Appendix summarizing the JDR cases from the standpoint of a variety of legal specialties, while highlighting the differences between JDR and ADR.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".