Bibliographic record
Abstract
Abstract The word witch conjures up a black-cloaked figure with a pointed hat flying on a broomstick, often with green skin and a hooked nose: the epitome of feminine evil. Although this version of witches was popularised in The Wizard of Oz (1939) and commercialised in mid-twentieth-century North American Halloween costumes, conjecture is that it originated from the slightly greenish hue of applying botanical remedies, or the appearance of witches who had endured bruising and painful torture. During the height of the European witch hunts (about 1450–1750, with the greatest intensity 1550–1650), an estimated 40,000–60,000 witches were executed (Levack, 1987). Although some men factored into this death toll, estimates are that 75–80% of witches executed were women (Gibbons, 1998). Fear and persecution of witches exists globally, dating to Ancient Rome, but the more systematic purges were the result of complex forces, including rapid social and economic changes of the Early Modern era, the Reformation, the Little Ice Age and the Plague (Federici, 2014; Golden, 2006). Those perceived as witches, often impoverished, older, single women, were easy scapegoats for society's ills. In recent decades, the depth and accuracy of archival research into witch hysteria have improved. Drawing on this research, this chapter examines the place of witch persecutions in the contemporary context. Although people often recognise the injustice of these persecutions, few countries have granted legal pardons or erected memorials to their victims. Why is the acknowledgement of these injustices so slow coming? What fears about witches do we still harbour?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".