The Fiscal Burden of the Young and the Elderly
Bibliographic record
Abstract
Population aging has become one of the major concerns of policy makers in many industrialized countries. Resulting from the low fertility rates of the past forty years [Denton, Feaver and Spencer 2002], it will affect both economic growth, the long-term viability of private and public pension plans, and the sustainability of existing fiscal structures [OECD 2001]. In expressing these concerns, reference is often made to dependency ratios, especially dependency ratios for the elderly. These ratios, however, have no direct economic or fiscal meaning because they simply relate a population subset (those who are deemed to be “dependent”) to another subset of the population (the supporting population). In order to attach a fiscal meaning to population aging, we must develop indicators that incorporate the fiscal impact of changes in the age composition of the population within a given fiscal structure. An approach to this issue is now presented and is applied to the Canadian experience during the period from 1989 to 2001.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".