The Pension Governance Deficit: Still with Us
Bibliographic record
Abstract
A 1997 investigation into the quality of pension fund governance uncovered a wide-spread board competency problem. This follow-on study analyzes the findings of a new survey on pension fund governance, to which an international group of 88 senior pension fund executives responded. Survey responses indicate that the board competency problem has not disappeared. As was the case in 1997, we found a positive correlation between governance quality and fund performance. The new results also suggest that selection processes for members of the board of governors continue to often be haphazard. Self-evaluation of board effectiveness continues to be the exception rather than the rule. Weak oversight functions continue to lead to difficulties in sorting out the competing financial interests of differing stakeholder groups, and result in organization dysfunction. Examples are lack of delegation clarity between board and management responsibilities, board micro-management, and non-competitive compensation policies in pension funds. We recommend a number of specific actions to address the governance challenges that continue to face pension funds. We also recommend that regulators require pension funds to regularly report on the actions they are taking to strengthen their governance processes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".