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Record W4310269107 · doi:10.4000/sdj.4573

Harsh Noise Industrial Games : À propos d’une pratique artistique industrielle du jeu vidéo

2022· article· fr· W4310269107 on OpenAlexaff
Frédérick Maheux

Bibliographic record

VenueSciences du jeu · 2022
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article analyse des créations vidéoludiques récentes à travers le prisme de la culture et de la musique industrielle, créations qui seront catégorisées comme « jeux vidéo industriels ». Face à une effervescence de la production de jeux menée par des concepteurs solitaires depuis 2015, l’auteur identifie comment des pratiques singulières de création vidéoludique peuvent être inspirées par des formes artistiques underground. Afin de guider cette réflexion, des entrevues ont été menées avec cinq créatrices et créateurs : Yames, Selfish Dream, Valerie Dusk, Stef Pinto et Andrea Pignataro. Le texte propose une analyse des correspondances entre leurs stratégies de production et celles de la culture industrielle, notamment en s’intéressant à une utilisation particulière du bruit (sonore, mais aussi visuel et spatial), ainsi qu’à la manière dont ces créations font office de bruit au sein de l’industrie dominante du jeu vidéo, venant ainsi fragiliser ses définitions canoniques et l’histoire technocentriste du médium.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.002
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.201
GPT teacher head0.294
Teacher spread0.093 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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