Is Aging Biology Ageist?
Bibliographic record
Abstract
The scientific questions we pursue are shaped by our cultural assumptions and biases, often in ways we are unaware. Here, we argue that modern biases against older adults (ageism) have unconsciously led aging biologists to assume that traits of older individuals are negative and those of younger individuals positive. We illustrate this bias with the example of how a medieval Chinese scholar might have approached the task of understanding aging biology. In particular, aging biologists have tended to emphasize functional declines during aging, rather than biological adaptation and population selection or composition processes; the reality is certainly that all these processes interact. Failure to make these distinctions could lead to interventions that improve superficial markers of aging while harming underlying health, particularly as the health priorities of older adults (autonomy, function, freedom from suffering, etc.) are often quite different from the goals of aging biologists (reducing disease, prolonging life). One approach to disentangling positive, negative, and neutral changes is to map trajectories of change across the life course of an individual (physiobiography). We emphasize that our goal is not to criticize our colleagues-we have been guilty too-but rather to help us all improve our science.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.017 |
| 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".