Bibliographic record
Abstract
This paper used Reiterative Truncated Projected Least Squares (RTPLS) to estimate the effects on life expectancy of an additional dollar of insurance premiums for 43 countries. The data shows a clear positive relationship between insurance and life expectancy with insurance premiums increasing much faster than the inflation rate. The relationship d(life expectancy)/d(insurance) fell by a statistically significant amount (at a 95 percent confidence level) for 35 of the countries (and the eight exceptions to this pattern had relatively short data series). By 2020, the last dollar of per capita insurance increased a US citizen’s life expectancy at birth by only 6 days, a citizen in the United Kingdom by only 9 days, a citizen in Switzerland by only 7 days, and a citizen in Luxembourg by only 1 day. With such small returns to insurance, an important question is, “Could a society gain more life expectancy by shifting money from insurance into alternative uses”?
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".