Informed Decision-making in Judicial Mediation and the Assessment of Litigation Risk
Bibliographic record
Abstract
While much has been written about how mediators can guide parties through impasse, the popular literature is less attentive to the method of litigation risk analysis and how it might fit inside judicial mediation. Opinions differ about whether and where a judge facilitating a settlement conference should allow an evaluative approach. This article reframes the focus from the judge’s style, to the litigant’s need to make informed decisions in any process. A litigation risk assessment can be adaptive, responsive to the environment created in judicial mediation. Within various mediation styles — even those which veer away from opinion-giving and evaluation — the judicial mediator can still help the parties develop realistic projections and measurements for comparison. Framing the conversation around a litigation risk assessment allows judicial mediators to reality-test while working to preserve judicial impartiality and party self-determination. The following discussion presents a practical framework for managing the risk assessment dialogue, and a spectrum of degrees of intervention, which allows judges and mediators to tailor the dialogue to fit their own mediation processes and styles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".