Bibliographic record
Abstract
There is now a broad consensus that workplace pension arrangements around the world are sick and in need of strong medicine. Pension coverage and adequacy are too low, and pension uncertainty too high. The prescription of some pension experts is to resurrect the traditional defined-benefit (DB) plan. Others say broad defined-contribution (DC) plan coverage is the cure. This article argues that we have to move from an “either-or” to an “and-and” mindset if we want to seriously improve global workplace pension coverage, adequacy, and certainty. Integrative thinking about these issues leads to pension arrangements that combine the best of both traditional DB and DC plans, and that minimize the impact of their less attractive features. However, redesigning the pension formula is only half the cure. We must also redesign the institutional arrangements through which workplace pensions are delivered. The ideal pension delivery institution has expertise, scale, and acts solely in the best interests of plan participants. There are far too few pension funds around the world today that can meet this triple test. Placed in a Canadian context, the first priority should be to fill the workplace pension gap for the some 4 million Canadian private sector workers without registered pension plans currently facing materially reduced post-work standards of living.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".