Liability Risk for Outside Directors: a Cross‐Border Analysis
Bibliographic record
Abstract
Abstract Much has been said recently about the risky legal environment in which outside directors of public companies operate, especially in the USA, but increasingly elsewhere as well. Our research on outside director liability suggests, however, that directors’ fears are largely unjustified. We examine the law and lawsuit outcomes in four common law countries (Australia, Canada, Britain, and the USA) and three civil law countries (France, Germany, and Japan). The legal terrain and the risk of ‘nominal liability’(a court finds liability or the defendants agree to a settlement) differ greatly depending on the jurisdiction. But nominal liability rarely turns into ‘out‐of‐pocket liability,’ in which the directors pay personally damages or legal fees. Instead, damages and legal fees are paid by the company, directors’ and officers’(D&O) insurance, or both. The bottom line: outside directors of public companies face a very low risk of out‐of‐pocket liability. We sketch the political and market forces that produce functional convergence in outcomes across countries, despite large differences in law, and suggest reasons to think that this outcome might reflect sensible policy.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".