SETTLEMENT COUNSEL: AN INNOVATIVE STRATEGY FOR THE MANAGEMENT AND RESOLUTION OF COMMERCIAL LITIGATION FILES
Bibliographic record
Abstract
Settlement Counsel is a negotiation structure that separates litigation and settlement roles allowing for the simultaneous advancement of litigation and negotiation on parallel tracks, by different lawyers. Although widely viewed as successful among its proponents, the settlement counsel model is not yet common in Canada. This article discusses the results of an interview-driven study of a small cohort of lawyers in Canada and the United States, describing the diverse ways that the settlement counsel model has been employed and highlighting its benefits and resistance points that have impeded widespread use. Reflections are offered as to how settlement counsel ideals might be used to construct responsive, client-centred approaches in lawyers' work: using client interests as a compass and employing risk assessments, mutual incentives, and accountability for results inside negotiation.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".