PENSION PAPERS The Public-Sector Pension Bubble: Time to Confront the Unmeasured Cost of Ottawa’s Pensions By
Bibliographic record
Abstract
Fair-value accounting reveals Ottawa’s employee pension obligations to be larger and more volatile than they appear, a problem shared by European and US state governments. The federal government’s net pension obligation under the fair-value approach stands at almost $208 billion – some $65 billion larger than reported in the Public Accounts; to keep pace with benefit accruals and stop the gap from growing, contributions in the latest fiscal year would have had to be almost double what was actually paid in. Taxpayers risk finding that responsibility to back-fill the funding hole falls to them – and potentially finding that fears of sovereign defaults by governments with opaque balance sheets and big exposure to public employee pensions drive up the cost of borrowing. Government employee pensions are rapidly emerging as a major fiscal problem. 1 Reducing these obligations is a priority for heavily indebted European countries, such as the United Kingdom, where a recent fair-value estimate of the unfunded obligations of national government pensions (Record 2009) put them at £1.1 trillion – some 40 percent higher than official figures. A similar look at US state governments puts their unfunded
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".