Assessing Delaware’s Oversight Jurisprudence: A Policy and Theory Perspective
Bibliographic record
Abstract
The recent financial crisis has triggered a renewed emphasis on corporate \ndirectors’ duty to exercise oversight. In contrast to the area of discrete \ndecision-making, where academics only rarely dispute the board’s almost \ncomplete insulation from liability, there is considerable resistance to the idea \nthat oversight should be subject to equally strict limits to hold directors \nresponsible. Yet, Delaware courts have made it clear that they continue to \nseverely limit, and not expand, directors’ oversight liability, leading Delaware \nin the opposite direction than that which many of its critics would advocate. \nBoth policy and theoretical considerations, however, support Delaware’s \ndecision to maintain strict limits on oversight liability. Such restraints protect \ndirectors’ ability to exercise independent business judgment, encourage risk \ntaking and board service by qualified individuals, and prevent directors from \nassuming the role as insurers of business risks. In addition, limits on \noversight liability reduce monitoring costs and promote efficient functioning \nof boards. Thus, as this Article argues, Delaware’s current solution works and \nstrikes the correct balance between directors’ accountability and authority.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".