Bibliographic record
Abstract
In the never-ending debate about finding an optimal pension model, many proponents start the discussion at extreme ends of the pension model paradigm. At one extreme is a traditional, fully guaranteed defined-benefit (DB) pension plan. In this plan, all of the risks are born by the plan sponsor given that plans are fully funded. While such plans are growing rare today that is the starting point for many in this debate. At the other extreme is a traditional defined-contribution (DC) plan. In this plan, all of the risks are borne by the worker participant. This, again, is a starting point for many in the pension model debate. Many classic DB and classic DC pension plans have not achieved their goals. This paper argues they should be replaced by pension plans that facilitate sharing of risks among all willing stakeholders, whether the plan is characterized as DB or DC. This paper proposes, as a starting point for all pension-plan model discussions, a “Common Ground.” If one is of a pro-DB persuasion, then the Common Ground model would be a Pooled Target Benefit DB pension plan. If one is of the pro-DC persuasion, then the starting point will be a large Collective DC plan. These plans have a lot in common and, since they can provide equivalent benefits for the same contributions, they should be viewed as being actuarially equivalent. Thus, by finding the common ground in the Great Pension Debate, we have also identified models for pensions that can provide all Canadian workers with significant retirement income security. With that accomplished, the question becomes whether one wants a bit more of a DB flavour and why or whether one wants a bit more of a DC flavour and why. This should make arriving at a consensus plan model much easier for all. We conclude that policies encouraging larger collective, pooled pension plans governed by independent management boards are very much needed to better serve Canadians. Such solutions are common in the public sector but need to be encouraged in the private sector.
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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 | 0.035 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 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".