On Listening as Analysis: The <i>Selfie Orchestra</i> Project
Bibliographic record
Abstract
Our paper addresses the notion of listening as a method of analysis. Our reflections stem from our development of an interactive sound art piece entitled Selfie Orchestra. This work is produced with sound, image, and GPS data gleaned from audience members’ cell phones. It features an interactive 3D simulation of the La Friche la Belle de Mai district in Marseille, France. We begin with a brief description of this project, highlighting our attempts to provoke expanded forms of listening through the technological innovations at the heart of the work. We then turn to a theoretical examination of listening as a method for reflection and research in the social sciences, with specific reference to Henri Lefebvre’s evocative concept of ‘rhythmanalysis.’ We reflect briefly on John Cage’s work 4’33” (1952) and Max Neuhaus’s Listen (1966) as precedent artworks that make space for attentive listening. We then contrast the open-ended interactivity promoted by these works with the more constrained forms we have been developing with Selfie Orchestra. We conclude with thoughts on how Selfie Orchestra demonstrates the strengths of listening as a method of analysis when the latter is understood following its ancient Greek conception as a form of ‘loosening.’
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".