Bibliographic record
Abstract
The witch-hunt of the Burgundian town of Arras in 1459-1460 was the first large- scale, state-sponsored witch-hunt of Western Europe. However, immediately following this witch-hunt we still find evidence of a reluctance to accept the realities of witchcraft among the populace, made plain in the official appeal record of the accused Seigneur Colard de Beaufort at the parlement de Paris. Scepticism of this kind stirred the Dominican cleric Johannes Tinctor out of retirement to write a vicious demonological treatise to convince the courts of Burgundy and France of the existence and dangers of a sect called vaudois, a term that had come to refer to witches. This essay closely examines Tinctor's heavy use of crusading imagery in his Invectives contre la secte de vauderie to justify and rationalize his arguments for duke Philip the Good of Burgundy and his court, a court renowned for consistent but empty promises of crusade and an elaborate culture bloated with an idealized infatuation with chivalric virtue and romance. In the autumn of the middle ages, when the traditional eastern crusade against "Saracens" had become frustratingly difficult to organize, what could be more appealing to a court so starved for crusade than a cry for war against an even greater enemy hiding amongst the populace, threatening Christendom from within?
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".