Longevity Extension from a Socioeconomic Perspective: Plausibility, Misconceptions, and Potential Outcomes
Bibliographic record
Abstract
In the last several decades, a significant amount of progress has been made in pursuits to better understand the process of aging and subsequently gain some level of control over it. Wide-ranging successes with gene therapy and increased comprehension of the genetic components of aging have also recently culminated in numerous successes in extending the longevity of animals and the first human trial of a gene therapy to extend life through telomerase manipulation is already underway, albeit on a small scale (Mendell et al. 2015; Bernardes de Jesus et al. 2012; Konovalenko 2014). In light of these recent accomplishments, bioethicists, sociologists, and philosophers have published a great deal of research on the subject, offering badly needed critiques, examinations, and discussions of the many potential positive, negative, and uncertain outcomes longevity extension could well necessitate. Their discussions are admirable and sorely needed, but the path to an even understanding of the potential consequences of longevity extension has lately become strewn with obstacles in the form of misplaced assumptions and a great deal of overtly emotional or instinctive rhetoric.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".