“Why are there so many steps?”: Improving Access to Blind and Low Vision Music Learning through Personal Adaptations and Future Design Ideas
Bibliographic record
Abstract
Music can be a catalyst for self-development, creative expression, and community building for blind or low vision (BLV) individuals. However, BLV music learners face complex obstacles in learning music. They are highly reliant on their learning environment and music teachers for accommodations and flexibility. Prior research identified the challenges faced by BLV musicians. Yet, limited research has addressed these challenges through the development of technology. Drawing upon the experience and suggestions of 40 BLV professional musicians, amateur musicians and music teachers (including sighted teachers with experience teaching blind students), we identified five themes: (1) Key Challenges of BLV Music Learning, (2) Personal Adaptations to Overcome Music Learning Challenges, (3) Perspectives on Current and Future Assistive Technologies, (4) Contention Between Braille Music and Auditory Learning, and (5) Role of Human Support for Music Learning. Together, these findings outline a path to make music learning more accessible to BLV people. To this end, we describe opportunities for enhanced audio cues for musical communication, recommend integrating vibrotactile feedback to aid music reading and design technology that supports independence and interdependence in music learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".