Recent Trends in Canadian Defined- Benefit Pension Sector Investment and Risk Management
Bibliographic record
Abstract
Defined-benefit (DB) pension plans account for the majority of employer pension fund assets. In recent years, a number of DB plans have become underfunded, in sharp contrast to the 1990s, when many plans had large actuarial surpluses. The deterioration in the financial health of DB plans has underscored various longer-term structural issues that could make it increasingly difficult for plan sponsors to manage the financial risks of these plans. Tuer and Woodman examine how funding deficits, a greater focus on plan liabilities, a low yield environment, and changing investment beliefs are influencing investment decisions in the Canadian DB pension sector. They review the funding of DB plans, changing views on the equity-risk premium, and the shift towards liability-centred approaches to investment and how these developments are affecting pension sector investment. They also consider additional influences on the pension sector, including the limited supply of long-term bonds, the elimination of the foreign-property rule, and the movement towards fair-value accounting and a financial-economics approach to actuarial valuation, as well as their implications for financial markets.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".