Public pension reform and the 49<sup>th</sup> parallel: Lessons from Canada for the U.S.
Bibliographic record
Abstract
Abstract Public employee pension systems around the world show remarkable diversity in design and execution. Among these, the U.S. defined benefit public pension system has drawn increased attention because of questions about the long‐term sustainability of many of the underlying pension funds – as well as concerns of equity between pension plan members, retirees, taxpayers, bondholders, and users of public services. The Covid‐19 pandemic introduced new fissures in state and local government finances, heightening the need to bolster long‐term public pension fund robustness. As an alternative model, the Canadian public pension system is widely respected. This was not foreordained. The authors trace difficult decisions undertaken in Canada in the 1980s and 1990s along with essential descriptive features of the Canadian Model. Using a novel primary dataset, the authors benchmark the 25 largest U.S. plans against their ten largest Canadian peers, exploring key issues in a paired analysis. The authors extract fundamental lessons from the Canadian experience, proposing a roadmap for reform of the U.S. public pension system. They argue that long‐term pension sustainability, once politically prioritized, must be built on equity and discipline in plan design, funding, and amortization of existing deficits. They emphasize the importance of legal framework, particularly joint sponsorship, alongside enhanced governance and unified legislation. They also draw lessons from the Canadian experience with respect to enhanced investment organizations and investment strategies.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".