Bibliographic record
Abstract
This paper begins with a discussion of how "good" and "bad" predictions about litigation risk can affect a negotiation process. It explores how thorough predictions are often missing in the way that lawyers and clients prepare for, and navigate through, their negotiations. Drawing on a recent study of lawyers and law students, this paper summarizes a simple framework for conducting a thorough risk assessment, and then examines the way that it can be used to support the pursuit of settlement. Two conclusions emerge from the study, and in particular from the observation of how law students negotiated a hypothetical civil litigation file. A risk analysis can ground the negotiator and client with a well-prepared reference point (or BATNA, discussed further below) and help identify the bargaining zone, adding strength to decision-making. Further, it can reduce adversarial posturing and even build trust and transparency in negotiation, assisting in the construction of a problemsolving process. This paper seeks to contribute to the development of best practices around the use of risk analysis, and the quest for more responsive and earlier settlement outcomes for clients.
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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 | 0.043 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".