From witchcraze to bitchcraze: a comparative dimension between the malleus maleficarum and Hustler magazine
Bibliographic record
Abstract
This thesis examines the Malleus Maleficarum and Hustler magazine for themes they may have in common. The purpose of this comparison is to display a manner in which women have been constructed and reconstructed within different social cultural contexts, maintaining similar personifications, specifically the personification of women as the witch/bitch. Qualitative content analysis was employed to glean the comparative dimension between the two publications. There were four categories of inquiry: 1) damage caused to male genitalia or reproductive capacity, 2) the disgusting and repugnant nature of women, 3) the insatiable sexual desire of women, and 4) the use of the words witch, bitch and synonymous terms. Comparisons between the Malleus and Hustler were found in each category. These are discussed as well as some differences between the two publications. The evidence supports the proposition that some of the images of the witch in the Malleus are reconstructed in Hustler. The latter usually does not refer to women as "witches" but the word "bitch" is found to be used multiple times per issue. The significance of the reconstruction of the witch to bitch is examined, as is the use of ambiguous terms. The censorship argument is also addressed and this avenue of intervention is suggested as counter-indicated and inappropriate.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".