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Record W2749613338 · doi:10.5206/notabene.v10i1.6613

The Synchretic Network: Linking Music, Narrative, and Emotion in the Video Game Journey

2017· article· en· W2749613338 on OpenAlexvenueno aff
Ivan Mouraviev

Bibliographic record

VenueNota bene · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
Fundersnot available
KeywordsInteractivityNarrativityNarrativeVideo gameNarratologyGame studiesMeaning (existential)MusicalSet (abstract data type)ArtVisual artsPsychologyAestheticsMultimediaComputer scienceSociologyMedia studiesLiterature

Abstract

fetched live from OpenAlex

In the 2012 video game Journey, music is an important component of the playing experience. This study adopts an interdisciplinary approach, drawing on narratology, semiology, and film-sound theory to examine the relationship between music, narrative, and emotion in Journey. After first discussing video games’ interactivity in general, philosopher Dominic Lopes’ theory of digital art is presented as a means of articulating the interactive aspects of Journey’s soundtrack. Theories set out by scholars Jochen Kleres and Michel Chion—which deal with the narrativity of emotions and audiovisual meaning, respectively—are then integrated to produce the “synchretic network”: a theoretical framework for analyzing the effect of the juxtaposition of music, moving images, and an emotional response that occurs when a viewer or player engages with audiovisual art. This is followed by an analysis of a personal experience of Journey using the synchretic network to understand how the game’s music performs narrative functions. Finally, this study reflects on the synchretic network and its potential to be broadly applicable, including in the study of other audiovisual media such as film.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.312
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2017
Admission routes1
Has abstractyes

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