Fair Innings: An Empirical Test
Bibliographic record
Abstract
The fair innings principle states that fairness requires allocating life-saving treatments to younger rather than older patients when each would gain the same extension in longevity. It is motivated by the notion that older patients have already benefited from a longer life and so have less claim to scarce treatment resources than younger patients who have not yet lived their "fair innings." The principle can be theoretically justified by a prioritarian social welfare function applied to lifetime wellbeing. We conducted an online survey to test whether there is support for the principle in the general population (in France). We find substantial but not universal support. When choosing to allocate a treatment that would provide the same life extension to an older or a younger patient, about one-half the respondents would allocate the treatment to the younger patient while about one-third are indifferent to which patient is treated and about one-fifth would allocate treatment to the older patient. Holding the life extension to the older patient fixed, decreasing the life extension to the younger patient decreases (increases) the fraction of respondents that would allocate treatment to the younger (older) patient. These results highlight the tension between principles of equal treatment and of giving priority to those who are worse off that confound healthcare policy.
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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 | 0.069 | 0.371 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.071 | 0.004 |
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".