Skating to Where the Puck Will Be: Exploring Settlement Counsel and Risk Analysis in the Negotiation of Business Disputes
Bibliographic record
Abstract
The infusion of ADR into legal training has been shaped by its ‘alternative’ identity, bringing with it a tendency to reinforce schisms: rights or interests, adversarial or collaborative approaches, litigation or settlement. Either-or thinking served its purpose in the early years but falls short especially with large-scale business files, where litigation looms and sophisticated clients expect multiple-pronged protective strategies. This article explores ways that settlement-oriented lawyers must function inside (rather than reject) the litigation regime. In particular, it introduces the author’s empirical study of two developments in the realm of business litigation: the “settlement counsel model” and the employment of litigation risk analysis methodologies. These developments are located within the larger theoretical frame for the lawyer's work, examples of unbundled legal services and planned early 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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".