From Sanitation Science to Geroscience: Public Health Must Transcend ‘Folkbiology’
Bibliographic record
Abstract
Abstract Folkbiology refers to people’s everyday understanding of the biological world. The early twentieth-century pioneers of public health C.-E.A Winslow (1877–1957), and his mentor H. Biggs (1859–1923), conceptualized public health as the ‘purchasable’ science of preventing disease and death from unfavorable economic and living conditions. Their ideas were foundational in shaping public health’s strategy of a ‘war against disease’ (Winslow, 1903), a strategy that was very successful in preventing the early-life mortality risks from infectious diseases, and was eventually extended to combating the chronic diseases of late life (like cancer). However, the initial framing of public health, through the lens of sanitation science, was predicated upon folkbiological premises that geroscience must abate in order to direct public health interventions toward the goal of improving the quality of life for older persons in the twenty-first century. Three folkbiological premises of sanitation science’s ‘war against disease’ are identified and critiqued: (i) the belief that health is the ‘normal’ condition of the human mechanism and disease ‘unnecessary’; (ii) the belief that the proximate causes of disease are the only modifiable risk factors public health interventions can alter; and (iii) the belief that the rate of biological aging is universal.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".