Mediation by Judges: A New Phenomenon in the Transformation of Justice
Bibliographic record
Abstract
This article has three principal parts. In the first, we present an overview of judicial mediation and how it responds to some of the perceived problems with the classical model of adjudication. In this analysis, we draw especially on the experience with judicial mediation at the appellate level at the Quebec Court of Appeal. In the second part, we examine the unfolding of the mediation process itself, using an annotated guide to judicial mediation to address broader issues of both practical and theoretical concern. In the third part, we consider the crucial question of ethics in mediation, signaling some of the problems in applying ethical models developed in the context of classical adversarial litigation and advocacy to mediation. Finally, we conclude by suggesting some continuing challenges and subjects for further study.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.074 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| 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".