MétaCan
Menu
← Back to cohort
Record W3035807395 · doi:10.24908/iqurcp.14048

Dungeons & Dragons & Drama

2020· article· en· W3035807395 on OpenAlexvenueno aff
Darby Huk

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
Fundersnot available
KeywordsDramaAdventureMAGIC (telescope)PsychologySocial psychologyLiteratureArtComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Players sit around a table. A group of adventurers pause in their pursuit of escape. Stunned, they stare at the die that just rolled poorly, resulting in the loss of a dear friend, his throat ripped out because they could not save him. The players mourn the death of a fictional character who only ever existed within the game. Dungeons & Dragons (D&D) is a popular role-playing game illustrating the interconnectedness of drama, performance, and games. My presentation will examine this relationship, identifying factors from gameplay that suggest how performance fosters success in D&D for both actual players and fictional characters. Research into dramatic theory and game theory reveals how interdisciplinary concepts such as the “magic circle”, the “lusory attitude”, and uncertainty can apply to elements of D&D (Salen and Zimmerman, Suits, Costikyan). Data collected from in-person observation of D&D sessions, coding participants’ behaviour, and watching for instances of performance (e.g. voice change, pronoun switches, or mimetic gesture), has been combined with theoretical research to determine elements that better facilitate success in the game/campaign. These elements range from emotional situations that provoke players, to forms of invitations encouraging participation (Isbister, White). I have discovered that while in theatre performance acts as a vehicle for story, in D&D the story acts as a vehicle for performance. The in-game performance often facilitates fun between players, as well as leading them to success in the game, so a campaign that maximizes theatricality will not only result in more fun, but also more success. Works Cited Costikyan, Greg. Uncertainty in Games. MIT Press, 2013. Isbister, Katherine. How Games Move Us: Emotion by Design. MIT Press, 2016. Salen, Katie, and Eric Zimmerman. Rules of Play: Game Design Fundamentals. The MIT Press, 2004. Suits, Bernard Herbert. The Grasshopper: Games, Life, and Utopia. Broadview Press, 2014. White, Gareth. Audience Participation in Theatre: Aesthetics of the Invitation. Springer, 2013.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0870.013

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.

Opus teacher head0.216
GPT teacher head0.418
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInquiry Queen s Undergraduate Research Conference Proceedings→Same topicDigital Games and Media→French-language works237,207→