Organisation for Economic Co-operation and Development
Bibliographic record
Abstract
The issue of pension benefit security has returned to the foreground of both economic and political debate in many OECD countries- following high profile losses of pension benefits due to plan sponsors becoming bankrupt and leaving underfunded pension schemes. Some countries have dealt with pension benefit protection via strong funding rules (the route taken for example by the Dutch authorities). Two OECD papers examine other methods for increasing benefit security in retirement – via pension benefit guarantee schemes (such as the Pension Protection Fund recently introduced in the UK) and the position of pension creditors within insolvency proceedings (which has been examined, for example, in Canada). Pension Benefit Guarantee Schemes are insurance type arrangements- with premiums paid by pension funds- which take on outstanding obligations which cannot be met by the insolvent plan sponsors. Arguments for such schemes stem from ‘market failure ’ (with workers not fully understanding the trade off between pensions – deferred wages – and current income), and diversification – as most workers are highly exposed to the insolvency of the plan sponsor (in terms of current and retirement income) and cannot properly diversify this risk (particularly where the pension is funded by book reserves). However challenges to these schemes exist – mainly in the form of moral hazard and adverse selection – which are problems for all insurance contracts, and potentially in the form of systematic risk (as bankruptcies tend to
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.271 | 0.297 |
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".