Appropriation, Culture and Meaning in Electroacoustic Music: A composer's perspective
Bibliographic record
Abstract
This paper explores issues related to cultural appropriation in acousmatic electroacoustic music. Through its use of sound recording technology, acousmatic electroacoustic music facilitates a broad range of potential mechanisms for cultural appropriation, from the abstract (idea) to the concrete (sound object). But appropriating culturally identifiable material is not without its hazards, and the composer may face accusations of superficial exoticism, cultural offence, or the violation of personal or legal rights. To complicate matters for the composer, each listener will bring his or her own knowledge to their understanding of the meaning of the material. A similar reception effect occurs with any artistic medium, of course. But in electroacoustic music, the clarity and immediacy of high-fidelity recording and playback can strongly enhance the identifiability of the material, and, by extension, the audience's potential attachment to it. As illustrations, I refer briefly to several works, including three of my own acousmatic pieces, that have made use of appropriation. Through those examples, we consider both the broader issues noted above and some specific concerns about language and voice. The goal is to provide an overview of some of the opportunities and possible pitfalls of cultural appropriation in electroacoustic music, as well as a brief map of one composer's journey through that thorny landscape.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".