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Record W4245721297 · doi:10.32920/ryerson.14651688

Reverberations: how modern video game design relies on audio and haptic communication to enforce rhetorics of procedure and play

2021· preprint· en· W4245721297 on OpenAlexaff
Saunder Waterman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of King's College
Fundersnot available
KeywordsRhetoricHaptic technologyVideo gameStorytellingComputer scienceMultimediaScope (computer science)Game mechanicsLegendHuman–computer interactionNarrativeArtificial intelligenceArtLinguistics

Abstract

fetched live from OpenAlex

Modern video games are increasingly becoming a more mature and respected form of storytelling and art. The way in which games can interact with players provides those players with a control over their product that is unmatched by literature or cinema. Games communicate with players in many direct and indirect ways. This paper explores how audio and haptic modes of communication are employed by different types of video games to support both elements of gameplay and the themes and rhetoric that a game possesses. Specifically, this paper focuses on how Super Smash Bros. Ultimate and Legend of Zelda: Breath of the Wild express different aspects of control and procedural rhetoric through audio and haptic communication. This paper also takes into account philosophical concepts of play, widening the scope of the paper to consider why video games are an essential pastime to generations of players.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.024
Scholarly communication0.0130.014
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.046
GPT teacher head0.299
Teacher spread0.254 · 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
Published2021
Admission routes1
Has abstractyes

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