Analysis of Accessible Digital Musical Instruments through the lens of disability models: a case study with instruments targeting d/Deaf people
Bibliographic record
Abstract
Music educators and researchers have grown increasingly aware of the need for traditional musical practices to promote inclusive music for disabled people. Inclusive music participation has been addressed by Accessible Digital Musical Instruments (ADMIs), which welcome different ways of playing and perceiving music, with considerable impact on music-making for disabled people. ADMIs offer exciting possibilities for instrument design to consider and incorporate individual constraints (e.g., missing arm, low vision, hearing loss, etc.) more than traditional acoustic instruments, whose generally fixed design allows little room for disabled musicians inclusivity. Relatively few works discuss ADMIs in the context of disability studies, and no work has investigated the impact of different disability models in the process of designing inclusive music technology. This paper proposes criteria to classify ADMIs according to the medical, social, and cultural models of disability, then applies these criteria to evaluate eleven ADMIs targeting d/Deaf people. This analysis allows us to reflect on the design of ADMIs from different perspectives of disability, giving insights for future projects and deepening our understanding of medical, social, and cultural aspects of accessible music technology.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".