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Why aging research?

2010· article· en· W1609085910 on OpenAlexaff
Colin Farrelly

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsQueen's University
Fundersnot available
KeywordsHarmIntervention (counseling)DiseaseDutySuccessful agingMedical researchRisk analysis (engineering)PsychologyMedicineGerontologySocial psychologyPolitical scienceLawPathologyPsychiatry

Abstract

fetched live from OpenAlex

The American philosopher John Rawls describes a fair system of social cooperation as one that is both rational and reasonable. Is it rational and reasonable for societies that (1) are vulnerable to diverse risks of morbidity (e.g., cancer, heart disease) and mortality and (2) are constrained by limited medical resources, to prioritize aging research? In this paper I make the case for answering "yes" on both accounts. Focusing on a plausible example of an applied gerontological intervention (i.e., an antiaging pharmaceutical), I argue that the goal of decelerating the rate of human aging would be a more effective strategy for extending the human health span than the current strategy of just tackling each specific disease of aging. Furthermore, the aspiration to retard human aging is also a reasonable aspiration, for the principle that underlies it (i.e., the duty to prevent harm) is one that no one could reasonably reject.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.007
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.418
GPT teacher head0.479
Teacher spread0.060 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2010
Admission routes1
Has abstractyes

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