Bibliographic record
Abstract
This article reflects on a number of issues surrounding the appropriation of culturally identifiable sound material for artistic purposes – both overall or broader concerns and those that may arise particularly in conjunction with electroacoustic musical composition. More specifically, we explore questions potentially raised by three electroacoustic compositions recently commissioned by the Instruments INDIA project, a unique cultural partnership between Liverpool Hope University (represented by Dr Manuella Blackburn) and Milapfest (represented by Alok Nayak). Those three compositions were created exclusively with materials from an extensive library of Indian music performances, curated and recorded by Blackburn specifically for Instruments INDIA, and premiered in concert in Liverpool, UK, 20 January 2017. Following the broader discussion of relevant concerns, we briefly review some perspectives offered by the three composers (one of whom is the author), as they relate to cultural appropriation in general, and working with the Instruments INDIA sound library in particular.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.013 |
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".