Right Table, Wrong Idioculture: Examining the Impacts of TTRPG-specific Media at the Player Table Level
Bibliographic record
Abstract
Over the past decade and a half, there has been an explosion in the quantity and availability of media focused on tabletop role-playing games (TTRPGs). Episodes of highly-produced actual play series like Critical Role and Dimension 20 regularly get millions of views online and have spawned hundreds, if not thousands, of similar programs. Type in “Dungeons & Dragons” or “tabletop role playing” into your search engine of choice, and you will find copious amounts of meta content covering nearly every aspect of the hobby. This wide range of content caters to a broad audience — from the beginner player with zero real-world experience, all the way to the grizzled veteran Dungeon Master. The popularity of these media has brought more players to the game and has made it easier for new players to become socialized into the subculture. Currently, little research exists on the impacts of these media on the quality of players' games. Central to our discussion is Sidhu and Carter’s concept of “pivotal play,” wherein moments transcend the game and deeply impact the players even after they walk away from the table. We propose a framework necessary for moments of pivotal play to emerge, encompassing three layers of socialization crucial for each player: game competency, subcultural competency, and comradery. Through five qualitative, in-depth interviews with experienced TTRPG players, we sought to understand if this explosion of TTRPG-specific media has changed how players play TTRPGs. As a result of our research, we identify five key impact areas and suggest a new term (parasocial idioculture) to describe how actual play media may affect the process of player socialization.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".