Enhancing Music Accessibility through AI Systems for and with d/Deaf Individuals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Researchers have investigated visual and vibrotactile approaches to making music more accessible to d/Deaf individuals, focusing on music appreciation. However, these approaches often fail to help d/Deaf users fully understand and engage with the various musical elements of a song. My research addresses this gap through a series of design and evaluation studies with d/Deaf and non-d/Deaf participants. It begins with a formative study that identifies key attributes of song signing valued by the Deaf community. Building on this, a controlled study explores the use of disclosure statements to mitigate cultural misrepresentation. Finally, a systems study leverages Large Language Models (LLMs) to support the translation of lyrics to sign language. My next steps involve developing collaborative tools for song signers and facilitating culturally sensitive music experiences. These projects collectively bridge the gap between d/Deaf and non-d/Deaf communities, promoting intercultural understanding and expanding musical inclusivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it