Bibliographic record
Abstract
Since the report of an expert commission five years ago, Ontario pension standards have been updated multiple times in response – but there is still room for significant improvements. Further action is needed in three key areas: • the low participation rate of private sector employees in employer-sponsored plans; • lack of legislative flexibility for jointly sponsored public sector pension plan ( JSPP) designs to effectively manage the benefit/funding equation to ensure costs remain at a manageable and acceptable level; and •ineffective funding and surplus utilization models for traditional, single-employer, defined-benefit pension plans (DB SEPPs). With respect to sparse pension coverage, Ontario should enact the federal Pooled Registered Pension Plan (PRPP) legislative framework. However, it should be revised to require Ontario employers beyond a specified size to offer such a plan, and auto-enroll employees who would have the option to opt out. This would ensure the accumulation of assets needed to support retirement readiness and to create the scale needed to deliver on the promise of low operating costs. Furthermore, efforts should be devoted to integrate tax-free savings accounts into the PRPP package. In the public sector, taxpayers would benefit from the province incorporating target-benefit principles into the standards governing public pension plans, fixing the contribution rate for employers and ingraining risk management principles in the operation of these plans. This would help keep plan costs in line, reduce the potential for intergenerational inequities and broaden the general understanding of these plans. Finally, for DB SEPPs, funding levels and access to surpluses are the primary areas of concern. Current funding models could benefit from replacing the combined going-concern and solvency valuation model with a stronger going concern valuation model and a new margin reserve account. This would prevent overpayments made by employers during poor financial times to turn into trapped surpluses when times improve. The Arthurs Report’s objectives were to ensure affordable pension plans that are sustainable and operate within a clear set of unbiased rules. These goals are still within reach, provided the Ontario government implements new reforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".