Lessons from the Ontario Expert Commission on Pensions for U.S. Policymakers
Bibliographic record
Abstract
Professor Harry Arthurs recently served as the sole member of the Ontario Expert Commission on Pensions (OECP) and recommended some 142 recommendations for reforming and reinvigorating Ontario’s occupational pension system. Some of these pension reforms have already been enacted.This paper explores the process by which the Province of Ontario appointed a commission to study pension reform, the recommendations that were put forth in the Commission report, and why the government has implemented some of these proposals and not others since the report’s publication in 2008. After considering each of these questions, the article concludes by seeking lessons that can be learned from the Canadian experience as the United States continues to consider its own occupational pension reforms. More specifically, the objective of this article is to outline for the Employee Benefit Security Administration (EBSA) politically feasible methods to implement much-needed occupational pension reform in the United States.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".