The Ludic Bestiary: Misogynistic Tropes of Female Monstrosity in <i>Dungeons & Dragons</i>
Bibliographic record
Abstract
This article introduces the concept of the ludic bestiary, a game mechanic that the authors argue produces abject bodies. Using the “hag” in Dungeons & Dragons as a case study, the authors demonstrate how the game’s bestiary, the Monster Manual, functions as a tool of patriarchal control by defining, categorizing, and classifying the body of the female other as evil, abject, and monstrous. Importantly, the ludic bestiary not only exists as a core rulebook in Dungeons & Dragons but has also been remediated as a narrative-heavy submenu in several digital games. The authors find that the figure of the monstrous woman persists in games because of the widespread distribution of the Monster Manual to young men in hobby communities, the cultural influence of Dungeons & Dragons, depictions of monstrosity that blend the erotic with the maternal, and the discursive categorization and objectification of the female body by ludic systems.
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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 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".