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Record W2625190642

Listening to Noise: An Interactive Soundscape Installation that Transforms Place in the Service of Intangible Cultural Heritage

2015· article· en· W2625190642 on OpenAlexaboutno aff
Aaron Liu-Rosenbaum

Bibliographic record

VenueMaterial Culture Review / Revue de la culture matérielle · 2015
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSoundscapeActive listeningIntangible cultural heritageCultural heritageCultural heritage managementSound (geography)Field (mathematics)AestheticsArchitectural engineeringSociologyAcousticsHistoryEngineeringArtArchaeologyCommunication
DOInot available

Abstract

fetched live from OpenAlex

Emerging technologies offer unprecedented opportunities for creating and preserving intangible cultural heritage. At the same time, these technologies pose fresh challenges to both our understanding and preservation of cultural heritage as new practices emerge through the interchange between traditional practices and current technologies. In the field of music, these new practices have blurred an already tenuous distinction between music and noise, which requires a more inclusive definition of intangible cultural heritage with regard to sound. This article will examine the case of an interactive sound installation that engaged the local community with environmental noise in the form of a sound portrait of Quebec as a lens through which to explore the above issues.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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 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
Published2015
Admission routes1
Has abstractyes

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