The Devil’s Curses: The Demonic Origin of Disease in the Sixteenth and Seventeenth Centuries
Bibliographic record
Abstract
The witch hunts at the beginning of the early modern era greatly broaden the question of the demonic origin of certain diseases as attested by the Bible, which at several points shows a demon capable of acting, by divine permission, on bodies and spirits. Until that time, beneficial or evil spells cast by witches on men or animals had a mysterious origin, and their effectiveness was not questioned. Beginning in the fifteenth century, these magical practices, which we find in all traditional societies, became extremely suspect: they could not but come from a pact with Satan; how else could the sorcerers provoke storms, kill people and animals, spread disease? The proliferation of Satan’s henchmen thus represents an immense threat. Vying in evil, during the sabbath sorcerers prepare powders and unguents and receive the power to make the one they designate as their victim fall violently ill by a single gesture or word. They are also able to send demons into the bodies of the possessed. All direct contact with them — true agents of contagion — runs the risk of bewitchment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".