Columbia Law School Roundtable on Public Aspects of Private Equity
Bibliographic record
Abstract
The mandate of the broader private equity “ecosystem” goes well beyond earning competitive returns for the limited partners and their beneficiaries. After noting that PE investing is encountering ever larger “headline” and social risks, the panelists were in complete agreement that LPs should exert greater pressure on PE sponsors to take account of and try to address negative externalities when buying and operating their portfolio companies. Bain Capital's Double Impact Fund, for example, while always looking for ways of increasing profits and reducing risk, sets out to have a positive influence on its non‐investor stakeholders, including employees. To that end, Bain develops and tracks company‐specific metrics linked to positive outcomes, and then links those metrics to management compensation. And the director of ESG programs at the International Limited Partners Association points to ILPA's programs for diversity and inclusion as a promising model.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.036 | 0.021 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".