Multi-tier and Mixed-Method Dispute Resolution in Canada
Bibliographic record
Abstract
This chapter traces ADR’s rapid progress from the fringes to the centre of Canadian dispute resolution practice. Historically, Canadian lawyers and judges were suspicious of arbitration and mediation—mostly indifferent but sometimes outright hostile. Today, the landscape is radically different. Arbitration, mediation, and other forms of ADR are frequently chosen by parties and robustly supported by legislation and the courts; pre-trial mediation is even mandatory in four provinces (and encouraged in the others). The popularity of multi-tier dispute resolution agreements has correspondingly risen, and they are generally enforceable in Canada. The courts tend to interpret multi-tier agreements to limit jurisdictional hurdles and promote efficient resolution of disputes. Med-arb and other forms of mixed-method dispute resolution have a shorter history in Canada, and many practitioners remain skeptical of their propriety and efficacy. But they are starting to catch on, with new med-arb rules and a professional designation introduced in 2019.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".