Pension Funds and Sustainable Investment
Bibliographic record
Abstract
Abstract Since its green shoots first emerged around 50 years ago, acceptance of environmental, social, and governance (ESG) considerations in institutional investing—especially in pension funds—has evolved with distinct shifts in investor preferences. This Pension Research Council volume traces these shifts and their implications, leading up to the present day. The book notes that investors have diverse reasons for devoting attention to ESG criteria when deciding where to invest their money. Some had religious motives, such as Quakers, who focused on values; this approach can offer some risk mitigation. Nevertheless, studies that look at whether divestment actually changes behaviors of companies show that this rarely occurs. Accordingly, this book offers a variety of distinct viewpoints from numerous countries, on whether, how, and when ESG criteria should, and should not, drive pension fund investments. Authors also find that policymakers should consider fund consolidation in private sector retirement systems, along with whether service provider incentives could be better aligned with sustainability incentives. For instance, boosting transparency in these markets would help generate better-informed policies, while providing beneficiaries with information relevant to their savings choices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".