Bibliographic record
Abstract
Museums have long been thought of as “quiet” spaces, in which visitors walk slowly through galleries to look at material cultures in glass cases. Music and sound have begun to pervade the quiet spaces of museums in the forms of aural installations and performance-based programs. They are no longer galleries for solely visual engagement, but loud spaces in which visitors and audiences listen to recordings, experience live performances, and participate by themselves singing and playing in workshops, classes, installations, and impromptu demonstrations. This article explores three case studies in exhibiting music. The first is the exhibition Ragamala: Garland of Melodies, which was on display at the Royal Ontario Museum and sought to demonstrate the fluidity between the South Asian arts. The second is an investigation of some of the formal and informal performance-based programming at the Aga Khan Museum. The last case study focuses on a future project, in which collectors of Indian audio cultures will submit contributions to help construct a history of sound in India. Each case study is motivated by a series of central questions: what constitutes “exhibiting music”? What are the broader implications of and consequences for exhibiting music in each case? How does exhibiting music in a museum impact a visitor’s experience? What kinds of new stories are told in exhibiting music and sound? The three case studies respond to these questions and provoke issues and possibilities for further critical inquiry. They show that museums are dynamic spaces with incredible potential to inspire multi-experiential engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".