MILLSTEIN CENTER‐ECGI CONFERENCE ON: Board 3.0: Bringing the Private Equity Model to Public Companies
Bibliographic record
Abstract
In a recent conference organized by Columbia Law School's Millstein Center and the European Corporate Governance Institute, law and econ scholars Jeff Gordon and Ron Gilson discuss with other academics and a remarkably varied and distinguished group of practitioners the possibility of “porting” elements of the private equity governance model to public companies to achieve what they describe as “Board 3.0.” The public company “monitoring” boards of the recent past—composed for the most part of part‐time, “thinly informed,” and “boundedly motivated” directors dependent upon corporate management for information about the company—are seen as evolving toward the “thickly informed, well‐resourced, and powerfully motivated” directors that Gordon and Gilson see as required to function more like “partners” in the business, helping steer management toward the long‐run value‐maximizing strategic and operating decisions. A number of the board members on the panel serve, or have served, on private as well as public companies. The consensus among this group was that PE boards function at higher levels than their public counterparts, accounting in significant part for the higher returns of PE. But as this group also noted, an ongoing “migration” of PE board members and practices to public companies, accomplished in part by reverse LBOs and PIPEs (private investments in public equity), is leading to more effective public company oversight and governance. Along with deeper specialized knowledge of critically important operational issues, PE directors are also said to have a comparative advantage in designing pay‐for‐performance incentives for operating managers. What's more, as one panelist pointed out, PE's need to focus on and prepare for “exit” from day one has the paradoxical effect of sharpening managerial attention on the long‐term future and the amount and kinds of investment needed to ensure it.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 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".