Roll for initiative : role playing & playing a role — queer representations in D&D
Bibliographic record
Abstract
At its core, Dungeons and Dragons immerses players in a dual reality. Led by a storytelling Dungeon Master (DM), players toggle between the immediate reality of the gameplay and the fictional narrative setting of the game’s campaign. Each player creates a character through which to navigate the fictional narrative setting. For many, character creation offers a means to imagine and assemble a collection of traits that go far beyond what is permissible outside the fictional world. Working from a theatre and performance studies perspective, I explore the contours of these traits and the details of the costumes, properties, and other theatrical elements that shape the characters. More specifically, I focus on how these traits and choices connect with minoritarian gender and sexual identities outside the game. I first consider a recent play and performance, both of which feature D&D and explorations of queer identity: Qui Nguyen’s 2011 play, She Kills Monsters (SKM); and DNDQ Live’s touring (2021-present) production of Dungeons and Drag Queens (DNDQ). Both examples feature D&D gameplay in which themes of gender and sexual identity are central to their young adult characters’ identities. Grounded in the theories of Judith Butler, Jose Esteban Muñoz and Bo Ruberg, I highlight key themes and actions in these plays that suggest how D&D gaming can foster positive expressions of queer identity. In chapter three, I share data from voluntary research interviews I conducted with self-identified queer, young adult (18-30) D&D players. In these interviews I asked questions aimed at understanding the interviewees lived experiences with D&D gaming, including game performance and in-scene choices, particularly in light of consciously expressed gender and sexual identity. Using Virginia Braun and Victoria Clarke’s 2022 reflexive thematic analysis, I coded and analyzed the data to identify themes describing how players understand and navigate the dual realities at play within the game and their individual sense of identity outside the game. In light of the evidence shared in both chapters, I conclude by arguing that D&D can offer generative opportunities for members of LGBTQ2SIA+ communities to consider constructs like gender and sexual identity through their explorations in game performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".