Bibliographic record
Abstract
Actuaries and others often use ratios to determine historical changes and to make predictions about future conditions. This paper considers the usefulness of using population ratios to predict the future financial impact on social support systems. I define two common population ratios and show the direction of the predicted financial impact on social support costs of population aging using these ratios. I present the analysis of two researchers, Spijker and MacInnes, and note how their analysis contradicts the predictions indicated by the common population ratios. I consider differences between social support systems with respect to health and long term care and those providing retirement income. I consider two ways in which Sweden is taking steps to make the burden indicated by population ratios more bearable, with respect to retirement income. Unfortunately Sweden’s calculation of the assets is flawed and so the adjustments required by the ABM are unfairly advantageous to pensioners and unfairly disadvantageous to future contributors. However, a concept developed for the estimation of assets, turnover duration, is useful. I propose two new population ratios, refined to incorporate components of turnover duration, and illustrate this approach using information concerning the Swedish pension system. I propose four recommendations.
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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 | 0.019 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.041 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".