Research Handbook on Representative Shareholder Litigation
Bibliographic record
Abstract
Written by leading scholars and judges, the Research Handbook on Representative Shareholder Litigation is a modern-day survey of the state of this essential field. The book is an important and timely contribution by leading corporate law scholars, judges, and practitioners, seeking to better understand and explain the proliferation of shareholder litigation across the globe. It provides a cross-jurisdictional survey of litigation and empirical evidence on the recent evolution of these lawsuits, including in-depth analyses of several key forms of shareholder litigation.Its chapters cover securities class actions, merger litigation, derivative suits, and appraisal litigation, as well as other forms of shareholder litigation. Through in-depth analysis of these different forms of litigation, the book explores the agency costs inherent in representative litigation, the challenges of multijurisdictional litigation and disclosure-only settlements, and the rise of institutional investors. It also surveys how related issues are addressed across the globe, with a special focus on parallel forms of litigation in the United States, Canada, the United Kingdom, the European Union, Israel and China.This Research Handbook will be an invaluable resource on this important topic for scholars of corporate law, practitioners, judges, and legislators.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.022 |
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".