Corporate boards : managers of risk, sources of risk
Bibliographic record
Abstract
Notes on Contributors. Preface. Introduction (Robert W. Kolb) (Loyola University Chicago) and (Donald Schwartz) (Loyola University Chicago) Part I: A Factual Basis. 1. The Relationship Between Boards of Directors and their Risk Management Organizations: Are Standards of Best Practice Emerging? (Michael A.M. Keehner) (Columbia Business School) and (David R. Koenig) (Ductilibility, LLC) Part II: Is Risk Management by Corporate Boards Even Possible? 2. Risk Management, Chaos Theory, and the Corporate Board of Directors (Michael Potts) (Methodist University) 3. Anti-Social Norms, Risky Behavior (Reza Dibadj) (University of San Francisco) 4. Time-Inconsistent Boards and the Risk of Repeated Misconduct (Manuel A. Utset) (Florida State University College of Law) 5. Discussion (Sridhar Ramamoorti). Part III: Board Structure and the Management of Risk. 6. Theories of Governance and Corporate Moral Vulnerability (Greg Young) (North Carolina State University) and (Steve H. Barr) (North Carolina State University) 7. Mitigating the Exposure of Corporate Boards to Risk and Unethical Conflicts (Shann Turnbull) (International Institute for Self-Governance) 8. Supervisory Board and Financial Risk-Taking Behaviors in Chinese Listed Companies (Zhenyu Wu) (University of Saskatchewan), (Yuanshun Li) (Ryerson University), (Shujun Ding) (York University), and (Chunxin Jia) (Peking University) 9. Discussion (David R. Koenig) (Ductilibility, LLC) Part IV: Corporate Boards and the Management of Specific Risks. 10. Entity-Level Controls and the Monitoring Role of Corporate Boards (Donna J. Fletcher) (Bentley University), (Mohammad J. Adbolmohammadi). (Bentley University), and Jay C. Thibodeau (Bentley University) 11. Do Corporate Boards Care About Sustainability? Should They Care?(Steven Swidler) (Auburn University) and (Claire E. Crutchley) (Auburn University) 12. Executive Risk Taking and Equity Compensation in the M&A Process (William J. Lekse) (University of Michigan Dearborn) and (Mengxin Zhao) (University of Alberta) 13. Discussion (Tom Nohel) (Loyola University Chicago) Part V: Corporate Boards, Risk Management, and the Ethical Firm. 14. The Ethics of Risk Management by a Board of Directors (Duane Windsor) (Rice University) 15. Assurance and Reassurance: The Role of the Board (Barry M. Mitnick) (University of Pittsburgh) 16. Risk Disclosure and Transparency: Toward Corporate Collective and Collaborative Informed Consent (Denise Kleinrichert) (San Francisco State University) and Anita Silvers (San Francisco State University) 17. Discussion (John R. Boatright) (Loyola University Chicago) Index.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".