Transformative Education for Pension Fund Trustees
Bibliographic record
Abstract
Pension funds represent the primary investment pool for the Canadian economy with about $600 billion of assets, yet scant attention is paid to the education of trustees. This study is based on a survey of managers of the top 100 funds in Canada. It indicates that the average fund has one day of training per year for trustees and most of the courses deal with conventional matters such as fiduciary responsibility. Only 13% of the funds have training in social investment for trustees. This paper outlines the field of social investment in pension funds and discusses some current initiatives being undertaken by unions to become more involved in socially useful investment strategies through transformative education programs for worker trustees. Résumé Avec des avoirs d'environ 600 milliards de dollars, les caisses de retraite représentent la plus importante mise en commun de capitaux de l'économic canadienne. Malgré cela, on porte bien peu d'attention à la formation de leurs administrateurs. Cette étude a été réalisée à partir d'une enquête menée auprès de qestionnaires des 100 caisses les plus grandes du Canada. Elle montre que la caisse moyenne offre à ses administrateurs une journée de formation par année, dont les cours portent surtout sur des questions conventionnelles, telle la responsabilité fiduciaire. Seulement 13% des caisses offrent des cours de formation en placement éthique pour administrateurs. Cet article donne un aperçu de la question du placement éthique dans les caisses de retraite et discute de nouvelles initiatives mises de l'avant par les syndicats en matière de stratégies de placement responsable. Les syndicats veulent s'engager davantage par l'intermédiate de programmes d'éducation transformative destinés aux travailleurs-administrateurs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".