Laura Stokes. Demons of Urban Reform: Early European Witch Trials and Criminal Justice, 1430–1530.
Bibliographic record
Abstract
The witch hunts of early modern Europe first began in the western Alpine mountains in the first half of the fifteenth century. It was at this same time and place that ecclesiastical fears of devil-worshipping sects, stemming from the Inquisition's pursuit of heretics, merged with common fears of harmful magic. Historians have argued that it was the emergence of the composite idea of a witch as both devil-worshipper and practitioner of maleficium that provoked the witch persecutions. In this excellent study Laura Stokes makes important corrections to this narrative. She argues that it is a mistake to pre-judge the origin of the trials by defining witchcraft as diabolical sorcery and then using that definition to determine where and how witch-hunting occurred. Stokes identifies the earliest picture of a witch as arising out of fear of weather witches among inhabitants of the rural hinterland. But how did such a fear lead to mass persecutions in the cities?
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| 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".