Assumptions of Normality: How Three Women with a Disability Changed the Face of Music
Bibliographic record
Abstract
Since the last 30 years, women musicians with a disability have remodeled the laws to integrate disability in the professional musical world, changed the way music is presented, understood, and taught, and integrated music as a form of activism.In this chapter, I give three examples of women I interviewed, who transformed the musical landscape through their actions.Evelyn Glennie is the first woman to develop a career as a solo percussionist.She had to show her teachers that deafness would not prevent her from achieving her musical studies.In her TED Talk "How to truly listen" she explained the methods she used to learn music through vibrations in her whole body, that she often presents to music students today.Gaelynn Lea is an American folk singer, violinist, and public speaker, very present on musical stage since winning NPR's Tiny Desk Contest in 2016.By changing the traditional way of holding her violin, she proved that physical limitations do not mean musical limitations.Lachi is an American singer, songwriter, composer, and producer.She advocates for a better diversity, equity, inclusion, and disability awareness in the music industry.As a blind musician, she faced the lack of role model figure when she was progressing in her career.Today, she wants to hold this role for the next generation of artists with a disability.In conclusion, I situate these three artists in our society, through the lens of gender diversity in the music industry.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.056 | 0.063 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.007 | 0.017 |
| 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".