Hacking new musical instruments and considerations of disability in design
Bibliographic record
Abstract
We report our findings of an instrumental case study of the ‘New Musical Instruments Hackathon’, which was hosted by Monthly Music Hackathon New York City. Our article commences with an overview of research literature on hackathons in general and then proceeds with a discussion of research on making accessible musical instruments, which occurs in multiple fields. Following, we outline our methodological approach that employed video-recorded observations and semi-structured interviews to examine how participants displayed and discussed hacking new musical instruments, and how, if at all, they designed with disability in mind. Our findings provide a description of the various activities that took place over the course of the hackathon event, two vignettes that detail the working processes of participants working on projects, and participants’ responses to semi-structured interview questions. While we are situated in the field of music education, our theoretical framework is rooted in disability studies, and our findings from this study may be applicable to those with an interest in the intersection of disability, music and technology. Our analyses and discussion confirm how many of the activities that occurred within this hackathon align with previous research on non-music hackathons; however, there are some notable differences that may be attributable to music hackathons and/or this specific hacking community in New York City. Finally, we make clear the conspicuous absence of design discussions and actions that centre disability and how this issue might be addressed in future research and practice.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".