Towards a fair assessment of social security liabilities under pay‐as‐you‐go and partially funded schemes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article provides insights into methodological and measurement considerations and challenges from an actuarial and social security policy perspective with reference to actuarial valuation work undertaken in the recent period. It aims at supporting the global discussion to improve the transparency of the reporting of financial liabilities of social security schemes linked to employment‐based obligations (contributory), as these are often guaranteed by the government following social security funding rules such as pay‐as‐you‐go and partially funded approaches. The article supports the actuarial profession's engagement with experts in national accounting and public finance statistics towards providing improved guidance to national governments in presenting a fair and accurate picture of the financial position of their social security schemes with due and unbiased recognition of the social security policy approach decision of any given country. While the reflection of the financial position of social security schemes guaranteeing long‐term benefits payable for life is most important in terms of possible public finance implications, care must be exercised in adopting a valuation methodology and indicators that are not biased and which do not distort the interpretation of its financial position. In this respect, challenges remain and there is ample scope for refining methodologies and adopting coherent accounting approaches encompassing policy decisions for funding purposes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it