Making Pooled Registered Pension Plans Work for Ontarians: CARP Submission to the
Bibliographic record
Abstract
In June 2010, finance ministers finally acknowledged that Canadians were not saving enough for their own retirement and that governments had a role to play. The federal government introduced Pooled Registered Pension Plans (PRPPs) as a solution to the savings gap and finance ministers committed to considering a “modest ” Canada Pension Plan (CPP) enhancement. Since then, federal PRPP enabling legislation has been enacted and provincial legislation has been tabled and is in various stages of approval. In December 2013, however, the federal government refused to act on modest CPP enhancement, leaving most Canadians with few safe, robust ways to save for their own retirement. In the absence of comprehensive national pension reform and a modest CPP enhancement, Ontario has committed to a Supplementary Provincial Pension Plan in addition to moving ahead with provincial PRPP legislation. PRPPs are an improvement on the status quo, but as designed currently PRPPs may not go far enough toward helping Canadians prepare and save for their own retirement. The potential business for the private sector administrators of the new PRPPs is enormous while individual savers will continue to bear much of the risks associated with private savings vehicles. PRPPs could be a major wind fall for the banks and insurance firms. The ‘regulated financial institutions ’ will get monopoly access to a major source of new business, but Ontarians may not get the safe, robust savings plan needed to secure
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".