Bibliographic record
Abstract
ABSTRACT In the late seventeenth century, reports of vampire attacks began to emerge out of eastern Europe. These stories became the focus of a scholarly debate that centered on whether the events described in the vampire reports could possibly be true and, if so, how they ought to be explained. The reports presented a dilemma: although the events they described were implausible, the reports were often supported by testimony from reliable sources. This article will explore two eighteenth-century interventions in the vampire debate, and consider how they responded to this tension. Augustin Calmet carefully documented and examined the vampire reports, using established natural philosophical models for the study of strange phenomena. Despite Calmet's rational methods and skeptical conclusions, his work on vampires was vociferously attacked by Voltaire. I will argue that the encounter between Calmet and Voltaire illustrates their different ways of understanding the relationship among superstition, knowledge-production, and testimony.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".