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Record W2148647975 · doi:10.1017/s1355771811000434

Voicing Nature in John Luther Adams's <i>The Place Where You Go to Listen</i>

2012· article· en· W2148647975 on OpenAlexaff
Tyler Kinnear

Bibliographic record

VenueOrganised Sound · 2012
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNatural (archaeology)VoiceMeaning (existential)HumVisual artsPerceptionPresentation (obstetrics)Natural soundsArchitectureSound (geography)AestheticsSociologyHistoryComputer scienceArtAcousticsEpistemologyPerformance artArt historyPhilosophyArchaeologySpeech recognition

Abstract

fetched live from OpenAlex

John Luther Adams's The Place Where You Go to Listen (2006), a permanent sound-and-light installation at the Museum of the North in Fairbanks, Alaska, resonates strongly with the geography and ecology of the composer's place of residence. The audiovisual experience is generated through a computer programme that translates real-time data streams from geophysical events into sound and colour signals. The Place functions as an artistic mirror, absorbing data from natural phenomena and reflecting it back to the listener in a deliberately allusive way. As a result, those present are invited to raise their awareness to the ‘unheard vibrations’ of the natural world. Upon entering the installation, the listener perceives an ongoing, harmonically dense hum. Through immersion, he or she notices change in both the location from which sounds project and the properties of audio and visual signals. Drawing on information theory, this article investigates the process whereby Adams renders scientific data into an audiovisual presentation as well as the role the composer and audience play in attributing meaning to this environmentally driven work. By examining the communicative layers of the installation and exploring the perceptual tendencies of the listener, we can better understand how The Place raises environmental awareness.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 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

Citations3
Published2012
Admission routes1
Has abstractyes

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