The science of governance: A blind spot of risk managers and corporate governance reform?
Bibliographic record
Abstract
This paper identifies the science of governance as a crucial blind spot for risk managers, company directors, regulators and law makers. There is little evidence that law-makers, corporate governance reformers or risk managers apply the science of governance identified 60 years ago. As a result, there are no accepted criteria for identifying or measuring good or higher standards of corporate governance or identifying its relevance for managing risk. This paper identifies why the current top-down approach to governance and risk management is incompatible with the bottom-up approach found in biota to manage risk so as to sustain life in highly complex uncertain environments. A bottom-up approach allows investors and stakeholders to become co-regulators of the risks to which they are exposed. As the mission of regulators is to protect citizens, citizen involvement as co-regulators richly increases the ability of risk managers, directors, firms and their regulators to minimise the risk exposure of firms, their stakeholders and/or the financial system. The paper identifies the need for risk managers, company directors, regulators and law makers to acquire knowledge of how to apply the science of governance to manage risk on the most efficient and effective sustainable basis.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".