Bibliographic record
Abstract
Employer defined benefit pension plans have long been an important component of the U.S. retirement system. Although these plans are disappearing in the private sector – replaced by 401(k)s – they remain the prevalent retirement plan arrangement in the public sector. But these public sector defined benefit plans are currently under financial pressure, as two financial crises since the turn of the century have caused liabilities to soar and assets to plummet. The response so far among state and local plan sponsors has been to suspend or eliminate cost-of-living adjustments, cut back sharply on benefits for new employees, and raise employee contributions. Some states have also introduced a defined contribution component. While the cutbacks hav e sharply reduced future costs, they have been ad hoc and unexpected. The question is whether a more orderly and predictable way can be devised to share risks, and perhaps head off trouble in advance. The Netherlands certainly offers one model of risk s haring; this brief discusses an adaptation of the Dutch approach closer to home –namely New Brunswick’s Shared Risk Pension Plan introduced in May 2012. The discussion proceeds as follows. The first section reviews the problem of risk in employer defined benefit plans. The second section describes New Brunswick’s response – the Shared Risk design and the regulatory framework for supervising such plans. Th e third section discusses the response of union representatives of workers covered by the new program. The fourth section considers what lessons U.S. plans can draw from the New Brunswick approach. The final section concludes that the Shared Risk approa ch is an important evolutionary step, and potentially an attractive alternative to the traditional defined benefit plan design.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".