Bibliographic record
Abstract
This chapter concentrates on wise-women, who were healers and, to a lesser extent, midwives, 1 and who were often designated as ‘witches’. In the Early Modern era, these women could find themselves accused of practising witchcraft even though healing was their chief objective. The witch-hunt 2 as applied to wise-women can be interpreted as another step in the removal of women from healing. Wise-men and wise-women were important healers in Early Modern Europe and women retained a pivotal role in village medicine in the pre-industrial era. 3 In rural areas of Europe, amateur healers, many of whom were women, were ubiquitous. They cured all descriptions of illnesses with herbs, poultices, prayers and ointments. This traditional healing role was threatened during the Renaissance because at this time, ‘the first concerted efforts were made to remove medicine from the realm of popular culture and establish it as the preserve of a restricted profession’. 4 Furthermore, this was the period when ‘medicine and science lost their spiritual dimensions; as healers, magicians, and witches lost their claim to manipulate the spiritual forces of the world, the ground was prepared for a mechanization of the world picture’. 5 It was during this period that a number of strategies were taken to eliminate women and other ‘popular’ healers from the medical ‘profession’. One of these was the licensing of various practitioners. A second method was the prescription of university training — denied to women — for physicians. 6 Another involved the accusation of witchcraft against irregular practitioners, such as ‘old wives’. Women’s work as village healers and midwives and their methods of healing through spells and potions made them vulnerable to attacks from the emerging medical profession, the state and the Church. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".