Voicing Nature in John Luther Adams's <i>The Place Where You Go to Listen</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".