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Record W2946099621 · doi:10.1177/1555412019850059

The Ludic Bestiary: Misogynistic Tropes of Female Monstrosity in <i>Dungeons &amp; Dragons</i>

2019· article· en· W2946099621 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGames and Culture · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsYork University
Fundersnot available
KeywordsBestiaryMonsterNarrativeObjectificationArtNarcissismBoredomPsychologyLiteratureAestheticsPsychoanalysisPhilosophySocial psychologyEpistemology

Abstract

fetched live from OpenAlex

This article introduces the concept of the ludic bestiary, a game mechanic that the authors argue produces abject bodies. Using the “hag” in Dungeons &amp; 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 &amp; 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 &amp; Dragons, depictions of monstrosity that blend the erotic with the maternal, and the discursive categorization and objectification of the female body by ludic systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.259
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it