Bibliographic record
Abstract
Abstract We often think of music in terms of sounds intentionally organized into patterns, but music performed in signed languages poses considerable challenges to this fundamental definition. How can we analyze and understand sign language music? And what can sign language music tell us about how humans engage with music more broadly? Seeing Voices argues that music is best understood as culturally defined, intentionally organized movement, rather than organized sound. This redefinition of music means that sign language music, rather than being peripheral or marginal to histories and theories about music, is in fact central and crucial to our understanding of all musical expression and perception. Sign language music teaches us a great deal about how, when, and why movement becomes musical in a cultural context, and urges us to think about music as a multisensory experience that goes beyond the sense of hearing. Using a blend of tools from music theory, cognitive science, musicology, and ethnography, this book examines the history, cultural context, and analysis of a wide variety of genres of sign language music. It aims to center the musical experience and knowledge of Deaf persons, to bring the long and rich history of sign language music to the attention of music scholars and lovers, and to challenge the notion that music is transmitted exclusively from the hearing to the Deaf. Finally, Seeing Voices proposes that voice, rhythm, melody, and emotion are properties of music that are resilient across visual, kinesthetic, and aural modalities.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".