Bibliographic record
Abstract
Adjusting the valuation of life along the (i) person-specific (age, health, wealth) and (ii) mortality risk-specific (beneficial or detrimental, temporary or permanent changes) dimensions is relevant in prioritizing healthcare interventions. These adjustments are provided by solving a life cycle model of consumption, leisure and health choices and the associated Hicksian variations for mortality changes. The calibrated model yields plausible Values of Life Year between 154K$ and 200K$ and Values of Statistical Life close to 6.0M$. The willingness to pay (WTP) and to accept (WTA) compensation are equal and symmetric for one-shot beneficial and detrimental changes in mortality risk. However, permanent, and expected longevity changes are both associated with larger willingness for gains, relative to losses, and larger WTA than WTP. Ageing lowers both variations via falling resources and health, lower marginal continuation utility of living and decreasing longevity returns of changes in mortality.
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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 | 0.007 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".