Bibliographic record
Abstract
Unless U.S. corporations take steps to harden their assets against natural disasters exacerbated by climate change and prepare for the transition to a zero-carbon economy, they face the prospect of catastrophic risk to their assets and market values, damaging both shareholders and, particularly where there are systemic effects, society at large. But surveys indicate that directors of American corporations do not view climate change as an important focus for their boards. In contrast, directors in Australia and Canada consider climate change one of the top two priorities for their governments and one of the top challenges for their companies. Different standards of liability for directors’ oversight failures, despite shared common law roots, may explain the differing attitudes toward climate risks.\nIn Delaware, under In re Caremark International Inc. Derivative Litigation, failure to monitor corporate risks is reviewed for knowing disregard of duties under the duty of loyalty. The only cases that have survived dismissal have involved legal and accounting compliance failures, prompting leading lawyers to conclude that there is no duty to monitor noncompliance enterprise risks. In Australia, Canada, and the United Kingdom, on the other hand, directors are subject to liability for negligence in monitoring risks to the corporate enterprise, and their efforts are generally reviewed for reasonableness. As a result, leading lawyers in those other common law jurisdictions have opined that directors face a significant risk of liability for failure to monitor climate risks.\nIn short, Caremark has failed to encourage Delaware directors to be sufficiently attentive to one of the greatest risks of harm to their corporations in the twenty-first century. There are at least three ways that Delaware courts could clarify Caremark jurisprudence to improve directors’ incentives to be attentive to climate risks: (1) confirming that Caremark applies to compliance with laws that already require attention to climate risks; (2) clarifying that Caremark applies to failures to monitor significant risks to the business other than legal compliance risk; and (3) clarifying that the duty to monitor is, after all, a matter of the duty of care, subject to a gross negligence standard of liability, but that only the most egregious failures to monitor—those involving a knowing failure to satisfy the duty of care—establish a nonexculpable breach of the duty of loyalty.\nThe first approach is uncontroversial but limited. The second, applying Caremark’s oversight standard to enterprise risk management, has been unnecessarily controversial because it is framed as a question of institutional competence. When framed instead as an issue of expertise, it is apparent that there are several sources of expertise on which Delaware judges could rely to review risk-monitoring failures under the Caremark framework, including corporate expertise, third-party expertise, and social consensus. Finally, returning oversight to the duty of care makes intuitive sense, particularly in light of the common law approach, and can appeal to both proponents and critics of the Caremark standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".