EMPIRICAL ETHICS AND THE DUTY TO EXTEND THE “BIOLOGICAL WARRANTY PERIOD”
Bibliographic record
Abstract
Abstract The world's aging populations face novel health challenges never experienced before in human history. The moral landscape thus needs to adapt to reflect this novel empirical reality. In this paper I take for granted one basic moral principle advanced by Peter Singer — a principle of preventing bad occurrences — and explore the implications that empirical considerations from demography, evolutionary biology, and biogerontology have for the way we conceive of fulfilling this principle at the operational level. After bringing to the fore a number of considerations that Singer ignores, such as the probability that nonintervention will result in harm and the likelihood that different kinds of extrinsic and intrinsic harms can be prevented, I argue that the aspiration to extend the human biological warranty period (by retarding the rate of aging) is a pressing moral imperative for the twenty-first century. In the final sections I briefly address some standard objections raised against life extension and conclude that, while there may be some legitimate concerns worth addressing, they are not compelling enough to provide a rational basis for forfeiting the potential health and economic benefits that could be realized by extending the biological warranty period.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.060 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".