Uninformed Consent in TTRPGs: Communicating Expectations to Avoid Nightmare Game Master Horror Stories
Bibliographic record
Abstract
This article offers a qualitative analysis of social media such as Reddit, TikTok, Twitter, Facebook, and YouTube regarding the abuse of power by a dungeonmaster (DM) in tabletop role-playing game (TTRPG) gameplay. It uses NVivo to analyze stories documenting “nightmare dungeonmasters” (NDMs) in order to better understand what players mean by that term. The data is coded for themes such as power, abuse, boundaries, and consent. The first half of the article deploys critical discourse analysis regarding passive/informed consent and the violation/maintenance of social boundaries at the TTRPG table. The second half of the article aligns the stories related in the first half with safety tools that are seen as applicable for avoiding NDM behaviors and their correspondingly negative gaming experiences. Tools are sourced both from this primary research and also from a literature review. The spirit of ethical research within the gaming community serves its reader by supplying them with a better understanding of NDM phenomena, as well as safety tools that can be employed on behalf of player boundaries.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".