Terrible Crimes and Wicked Pleasures: Witches in the Art of the Sixteenth and Seventeenth Centuries
Bibliographic record
Abstract
Early modern representations of witchcraft have been the subject of considerable recent scholarship; however, three significant aspects of the corpus have not received sufficient attention and are treated independently here for the first time. This dissertation will examine how witchcraft imagery invited discourse concerning the reality of magic and witchcraft and suggested connections to contemporary issues through the themes of the witch’s violent autonomy, bestial passions, and unnatural interactions with the demonic and the dead. These three themes address specific features of the multifaceted identity of the witch and participate in a larger discussion that questioned the nature of humanity. Analysis of each issue reveals a complex, ambiguous, and often radically open treatment of the subject that necessitates a revision of how witchcraft imagery from this period is understood. Each understudied aspect of witchcraft imagery is explored through a series of case studies that have not appeared together until now. Previously unexamined artworks with inventive content are introduced and canonical pictures are examined from new perspectives. These images were created in the principal artistic centers, the Italian city-states, the German provinces, and the Low Countries, during the sixteenth and seventeenth centuries when the controversy over witchcraft was at its peak. Although they are few in number, these highly innovative images are the most effective and illuminating means by which to access these themes. These works of art provide valuable insights into important issues that troubled early modern society. Chapter 1 reveals how witchcraft imagery produced in the Low Countries is concerned with the witch’s violent rejection of the social bonds and practices upon which the community depends for survival. Chapter 2 examines how the figure of the witch was used to explore concerns about the delineation and transgression of the human-animal boundary. Chapter 3 exposes an interest in the physical possibility of witchcraft; artists questioned the ability of witches and demons to manipulate the material world. Issues include the witches’ capacity to reanimate dead bodies and create monstrous creatures. Together these images demonstrate active and meaningful engagement with the theories, beliefs, and practices associated with witchcraft.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".