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Record W2779692812 · doi:10.1017/s135577181700036x

Acousmatic Music as a Medium for Information: A case study of <i>Archipel</i>

2017· article· en· W2779692812 on OpenAlexaboutno aff
Guillaume Campion, Guillaume Côté

Bibliographic record

VenueOrganised Sound · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)Composition (language)SoundscapeVisual artsComputer scienceArtAcousticsLiterature

Abstract

fetched live from OpenAlex

This article discusses the inclusion of concrete informative elements within acousmatic music, in an attempt to mix acousmatic music and sound documentary into a form of socially engaged sound art. Inspired by existing sound practices that make strong use of the sonic reality, such as soundscape composition or radiophonic art, the authors explain how they aim to address socially relevant topics within pieces where music and information are considered of equal importance. To that end, they give a detailed description of their approach through the analysis of the composition process behind Archipel (Côté and Campion 2016), a 29-minute piece focused on the access to the waterfront in the city of Montréal, Québec. Through an alloy of interviews, sound recordings gathered on the shores of Montréal and typically acousmatic sound-processing and synthesis, the piece attempts to portray the challenges and opportunities encompassed by this topic. Having found the need to go beyond the acousmatic concert format for this kind of work, the authors also briefly discuss how they are currently expanding the project to include an interactive website and a mobile application that will complement the initial concert piece.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
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 head0.072
GPT teacher head0.424
Teacher spread0.352 · 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 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
Published2017
Admission routes1
Has abstractyes

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