From Legitimacy to Leadership: A Comparative Analysis of Environmental, Social, and Governance Reports from the World's Largest Private Equity Firms
Bibliographic record
Abstract
L'industrie des capitaux privés, qui dépasse les 14 milliards de dollars d'actifs sous gestion dans le monde, est bien placée pour contribuer aux objectifs de développement durable (ODD) des Nations unies. Les auteurs analysent les rapports environnementaux, sociaux et de gouvernance de 33 entreprises de capitaux privés en 2020, y compris Brookfield Asset Management, la plus grande entreprise de capitaux privés au Canada. Ils examinent les ODD les plus populaires (particulièrement l'ODD-13, l'action climatique), les tendances géographiques et industrielles, les modes d'intégration des ODD et la comparaison entre Brookfield et l'ensemble du groupe. Les résultats ont révélé que les entreprises de capitaux privés ont neuf façons d'intégrer les ODD à leurs processus d'investissement. Brookfield déclare un fort engagement envers l'ODD-13 et souligne régulièrement les facteurs climatiques à une plus grande fréquence que les autres entreprises. Bien que les rapports de certaines entreprises incluent les placements de portefeuille respectueux des ODD, dans bien des cas, rien ne démontre qu'ils ont été retenus de manière intentionnelle avant les investissements.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".