Judicial Mediation in British Columbia: Moving Towards a More Effective Process
Bibliographic record
Abstract
My thesis examines judges acting as mediators in the court process. I wanted to examine how judicial mediation compares to private mediation through identified best practices. Styles of mediation are examined looking at evaluative mediation, facilitative mediation, transformative mediation and interest-based mediation. The rules of court dealing with judicial mediation in British Columbia are then examined to identify how judge lead mediations become part of the court process. Identified best practices of mediation are set out with an emphasis on process, neutrality, communication, emotions, culture and continuing education. These identified best practices are then compared and contrasted to how mediation is incorporated into the court process. The interviews of judges from the Supreme Court of British Columbia are used as an entry to their particular views about the benefits judicial mediation has to parties engaged in the court process and to the court process itself. I conclude by identifying the important role judicial mediation has within the court process and how the prevalence of judicial mediation will likely continue in family law litigation. Given that judicial mediation will continue, I argue it is important that the judicial mediation process be as effective as possible to maximize the benefits to parties and the court system. To move towards this goal, I make a number of recommendations on how the judicial mediation process may be changed.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".