Liv Helene Willumsen. <i>The Voices of Women in Witchcraft Trials: Northern Europe</i>
Bibliographic record
Abstract
In this wide-ranging work, Liv Helene Willumsen examines unpublished court records of witchcraft trials to foreground the voices of women accused of witchcraft in early modern northern Europe.The book is organized geographically, with individual chapters exploring trials from the Spanish Netherlands, northern Germany, Denmark, Scotland, England, Norway, Sweden, and Finland.Willumsen analyzes the trials of three women from each of these eight regions from the late-sixteenth to the late-seventeenth centuries.Each chapter includes a brief overview of the historical context surrounding that region's witch-hunting period, followed by descriptions of the trials and analyses of the different voices that Willumsen has identified in the records, including those of the accused women, the witnesses, the interrogators, the law, and the scribes.Willumsen argues that secular court documents produced during witchcraft trials preserve the thoughts and beliefs of ordinary women and provide a crucial window into the experiences of women who have otherwise left behind no records of their lives.The thesis is expansive, contributing to diverse areas of scholarship, including the history of mentalities and the interrelationship between gender and witch hunting.In the Scottish context, it builds on the work of historians Sierra Dye, Lauren Martin, Diane Purkiss, and Emma Wilby, who have explored how witchcraft trials communicate details of women's lives, often in their own words.The book's innovation, however, is found in its comparison of 'close readings of source material in the original languages of eight countries' and in its narratological approach, which treats the text of the court records as the object of analysis (pp. 2, 20).Willumsen carefully selected detailed cases that clearly distinguished between the individual voices present in the courtroom, which allows her to isolate the words of the women on trial.Her measured analysis of different trial components, such as witness statements, confessions, and interrogations, renders a vivid picture of the accused women as outspoken and active members of their communities.This considerate treatment restores the unique personalities of these women and shows them as dynamic historical
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.014 |
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".