The Drake Music Project Northern Ireland: Providing Access to Music Technology for Individuals with Unique Abilities
Bibliographic record
Abstract
Across the UK, a growing number of charity organisations, social enterprises, academic researchers and individuals have developed music technology-based music workshops and projects utilising Accessible Music Technology to address the issue of access to music-making for people with disabilities. In this article, I discuss my ethnographic study of The Drake Music Project Northern Ireland (DMNI), a charity which provides music workshop opportunities in inclusive ensembles at the community level. My methodology of participant observation involved undergoing the training necessary to become an access music tutor for DMNI, attending workshops and conducting interviews with people throughout the organisation. Key findings were that consumer music technology devices that were not designed to be accessible to a wide spectrum of users could be made accessible through adapting them with other devices or different sensor interfaces more suitable for people with unique abilities and specific needs. Throughout my study I found that it was not in the design of music technology devices that made them accessible. Rather, meaningful music-making emerged through the interrelations between the access music tutors, workshop participants and the music technology interfaces in the workshop environment. The broader implications of DMNI music-making activities and effects on social inclusion are also discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".