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Listening to Fear

2010· book-chapter· en· W2489722057 on OpenAlexaff
Guillaume Roux-Girard

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsActive listeningSound (geography)Argument (complex analysis)Relation (database)Meaning (existential)Context (archaeology)Game mechanicsMetagamingComputer gamePsychologyComputer scienceMultimediaCommunicationGame theoryAcousticsNon-cooperative gameHistoryMathematics

Abstract

fetched live from OpenAlex

This chapter aims to explain how sound in horror computer games works towards eliciting emotions in the gamer: namely fear and dread. More than just analyzing how the gamer produces meaning with horror game sound in relation to its overarching generic context, it will look at how the inner relations of the sonic structure of the game and the different functions of computer game sound are manipulated to create the horrific strategies of the games. This chapter will also provide theoretical background on sound, gameplay, and the reception of computer games to support my argument.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.019
GPT teacher head0.283
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations27
Published2010
Admission routes1
Has abstractyes

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