What if there’s no such thing as “aging”?
Bibliographic record
Abstract
Are diseases caused by aging? What are the mechanisms of aging? Do all species age? These hotly debated questions revolve around a unitary definition of aging. Because we use the word "aging" so frequently, both colloquially and scientifically, we rarely pause to consider whether this word maps to an underlying biological phenomenon, or whether it is simply a grab-bag of diverse phenomena linked more by our mental associations than by any underlying biology. Here, we consider how the presence of the colloquial word "aging" generates a cognitive bias towards supposing there is a unitary biological phenomenon. We ask what kind of evidence would support or refute that idea, and subsequently show clear evidence at multiple levels that aging is not a unitary phenomenon. In particular, the known aging pathways lead to heterogeneous outputs, not a single coordinated phenomenon. From levels ranging from cellular/molecular to clinical to demographic to evolutionary, we show how the supposition that aging is a unitary phenomenon can mislead and distract us from asking the best questions. For major sub-disciplines of aging biology, we show how going beyond the notion of unitary aging can hone the paradigm and help advance the pace of discovery.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".