Winners and Losers : The Inequities Within Government-sector Defined-benefit Pension Plans
Bibliographic record
Abstract
There are little-acknowledged yet striking inequities built into the payout formulas of defined-benefit (DB) pension plans, which are typically provided to government employees across Canada. An analysis of representative DB plans shows they systematically transfer income away from groups of employees in occupations with slow wage growth to employees in occupations or careers with higher wage growth rates; this often means from low-income clerks to high-income deputy ministers. The winners are “high-flying” employees who are likely to enjoy pensions that exceed the value of the accumulated employee and employer contributions in their “accounts” at retirement, while the losers are those who would be better off if they simply received the value of their contributions plus interest rather than rely on future payments from a discounted pension. However, public-sector DB plans could be redesigned to retain much of their appealing certainty and efficiency without redistributing retirement income among members to the extent that they now do.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.021 |
| 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".