Bibliographic record
Abstract
Over the past decade, the medium of tabletop role-playing games (TTRPGs) like Dungeons & Dragons has risen exponentially in popularity thanks to shows like Stranger Things and The Legend of Vox Machina as well as actual-play shows like The Adventure Zone, Critical Role and Dimension 20. Unlike video games and board games, the primary vehicle through which these games are played is language, making TTRPGs an enormous and as of yet rarely explored area of research for linguists and linguistic anthropologists. Participants must play, tell stories and build secondary worlds and selves all through interaction and conversation. Using tools from ethnographic field work, I investigate the features of the register as well as the conscious and unconscious linguistic strategies that are necessary to the function of TTRPGs. I observe and record three TTRPG sessions from pre-established recreational groups both in person and online and broadly transcribe relevant sections for linguistic anthropological analysis. Examination of these real-time improvised conversations reveals their performative nature as well as the conditions required for all players to agree that something takes place in the story that they are building. This paper outlines how players navigate the inevitable messiness and ambiguity that comes with multiple people trying to play a game and tell a story together and how they negotiate agency and control over that reality. Essentially, it considers how adults enact pretend play through language. It also describes the unique participation framework of TTRPG players as writers, characters and audience simultaneously. All of these elements serve to provide insight into this increasingly popular way of using language for entertainment and reveal some of why people find TTRPGs entertaining in the first place.
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.
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 | 0.007 | 0.036 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".