Bibliographic record
Abstract
Public-sector pension plans in Canada are generally large, efficient and well managed. Funding levels are healthy when compared to private-sector pension plans in Canada and public-sector pension plans elsewhere. .And yet, all is not well. There are large differences between the fair values of the pensions earned by public-sector employees and the “cost” of these pensions according to public-sector financial statements. These differences arise almost entirely from the pricing of guarantees. Specifically, the financial markets attach high values to the guarantees embedded in public-sector pension plans while government financial statements attach little or no value to these guarantees. This means that pension costs are materially understated and, as a consequence: • employees in the public sector are paid more than is publicly acknowledged and, in many instances, more • than their private-sector counterparts; • public-sector employees shelter more of their retirement savings from tax than other Canadians are permitted to shelter; and • taxpayers bear much of the investment risk taken by public-sector pension plans while the reward for risk-taking goes to public employees as higher compensation. Private-sector pension accounting standards long ago rejected the premise at the heart of today’s public-sector accounting standards – that the cost of a fully guaranteed pension depends critically upon the rates of return that a pension fund can earn on risky investments even though the pension itself is totally unaffected by these returns. Public-sector accounting practice recognizes, today, the returns that a pension fund might reasonably expect to earn as a reward for future risk taking. These returns are recognized long before the risks are taken and used to reduce the reported cost of employee pensions. As a consequence, the reward for future risk-taking goes to employees who, because their pensions are fully guaranteed, take no risk. Future taxpayers, on the other hand, will be expected to bear risk without fair compensation. Essentially, we have devised a complicated way to transfer wealth from future taxpayers to current plan members. The good news is that once the accounting problem is recognized for what it is, the solution becomes obvious. The risks that taxpayers are being asked to bear without compensation should be transferred, in whole or in part, to the plan members on whose behalf these risks are being taken. This can be accomplished in a variety of ways. Benefits can be tied to funding levels and/or to the performance of pension funds. Employee contributions and/or salaries can be tied to the cost of funding their pensions. Many provincial governments have already started to move in this direction.
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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 | 0.032 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".