Paracelsus’ 16 th Century Philosophy Applied to Current Evolutionary Genetics
Bibliographic record
Abstract
Paracelsus contributed greatly to medical philosophy in the early sixteenth century, yet his reputation was so tainted by his hypocrisy that he left few followers and is often forgotten. Many aspects of his teachings, however, can be applied to current theories governing evolutionary genetic research. His claim, “Where diseases arise, one can also find the roots of health” hints at the intimate relationships between health and disease that are the foundations of fascinating research. In many devastating medical cases, it has been found that expression of one genetic disease can confer resistance for another. Sicklecell anemia sufferers have an increased resistance to malaria, cystic fibrosis is associated with decreased susceptibility to influenza, tuberculosis and cholera, and even the human immunodeficiency virus (HIV1) is theorized to have stemmed from a selection for resistance to the Bubonic Plague. These examples demonstrate the ambiguities in distinguishing between health and disease. While scientists today would likely scoff at Paracelsus’ dated medical rants, when they discover a disease favoured by natural selection, one of the first questions is how it could have conferred a benefit ancestrally. Applying Paracelsus’ theories to a discipline as contrary to evolutionary genetics demonstrates both the robustness of his claims, and the potential impact philosophy can have on medical, scientific and sociological questions surrounding challenging epidemics
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".