Music Technology, Gender, and Sexuality: Case Studies of Women and Queer Electroacoustic Music Composers
Bibliographic record
Abstract
This document aims to contribute to the established scholarship that highlights the role gender and sexuality has with one’s fundamental relationship to composition and music technology. The profession of electronic music composition and music production are strongly associated with notions of power and control, as much of this technology was built during the World Wars and Cold War. These aggressive views have created gendered language and metaphors in the field. Metaphors are the primary way in which we accommodate and assimilate information and experience to our conceptual organization of the world. It is at the source of our capacity to learn and at the center of our creative thought. I hope to continue the discussion of language, metaphors, and various approaches to composing and working with music technology through a historical overview of women’s achievements and difficulties in the electroacoustic community. Elainie Lillios, Jess Rowland, and Carolyn Borcherding were selected to be interviewed for this document. Each interview allows them the opportunity to discuss their music, their approach to composition and their use of technology as part of their artistic process, and to discuss their roles of educators and their approach to pedagogy to further contribute to the scholarship and history of electroacoustic composers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".