Robustness of Judicial Decisions to Valuation‐Method Innovation: An Exploratory Empirical Study
Bibliographic record
Abstract
Abstract: In shareholder litigation, judges and litigants choose from a menu of valuation methods that is evolving over time. What drives demand for new valuation methods? Does valuation method choice affect a judge's final appraisal? Is a judge more likely to favor the litigant whose valuation method coincides with the judge's if the other litigant uses a different method? Using a comprehensive hand‐collected sample of Delaware appraisal remedy shareholder litigation cases, we show that the distribution of judicial appraisal outcomes is insensitive to valuation methodology. Moreover, valuation method agreement between judge and plaintiff (or between judge and defendant) does not influence the judge's appraisal. In this sense, judicial valuation is robust to innovations in valuation technology. However, we find that judicial valuation method choice is contextual. The method a judge chooses depends on the fundamental attributes of the firm as well as the quality of litigants' proposed valuation estimates. We conclude that judges demand new valuation methods when the new methods are contextually more appropriate.
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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 | 0.075 | 0.422 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".