What Is Corporate Governance? Can We Measure It? Can Investment Fiduciaries Rely on It?
Bibliographic record
Abstract
Ranking or evaluating corporate governance has become a big business. Trillions of dollars of investment capital is now allocated with reference to third-party commercial scoring of firms’ corporate governance arrangements. Media outlets rank companies with the best governance, and proxy advisors score governance arrangements and use them to make voting recommendations. But what, exactly, is being measured? And do the resulting measurements tell investors anything useful about what is going on inside the corporation? When the empirical research is examined, there appears to be no relationship between corporate governance scores or ranking schemes and future corporate performance. These schemes also fail to identify companies that are likely to experience scandals or even terminate underperforming executives. This is the case whether we examine the work of commercial rating agencies, media outlets, “comply or explain” regulatory regimes, academic models, or Environmental, Social and Governance indices. How did we get to such an absurd situation? How did corporate governance measures become detached from the actual operational outcomes that we care about? Twentieth century corporate law scholars welcomed the theoretical coherence that was eventually provided by agency theory and the modern conception of corporate governance. Commercial providers of financial products found various market and institutional imperatives satisfied by the new way of thinking about companies. But unlike these other actors, fund managers owe fiduciary duties that should prevent them from relying on the evidently flawed modern approach to measuring corporate governance. In fact, bad measurements of corporate governance adversely impact the risk-adjusted returns of even well-diversified portfolios.
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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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".