Bibliographic record
Abstract
This article examines the creation of queer rhetoric through role-play to find the reparative value that Dungeons & Dragons (1974-) can potentially provide the queer communities. My work focuses on the concept of reparative play, an adaptation of reparative reading which was first proposed by Eve Kosofsky Sedgwick in 1995 (Sedgwick 2003). Reparative reading explores alternatives to heteronormative ideals through the act of reading. Instead of getting caught up in the problematic implications of a text, the alternatives are foregrounded (Sedgwick 2003, 137). Reparative play then expands reparative reading into the realm of play, where one explores the possibility for a sustainable queer livelihood through play (Vist 2018). I conclude with an observation of safety tools designed for tabletop RPGs, that enable reparative play.This work will be posited alongside an autoethnographic reflection of my own role-play experience as a means of demonstrating reparative play in practice. My work is founded on Sedgwick’s (2003) Touching Feeling, Kara Stone’s (2018) “Time and Reparative Game Design,” and Sarah Lynne Bowman’s (2010) The Functions of Role-Playing Games. These scholars observe role-play as a method of queer performativity and identity exploration. I propose that through the embodiment of a D&D character, set in a more accepting world, the players can enact reparative play to give an accurate and positive representation of themselves while promoting alternatives to heteronormative culture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".