Music Beyond Sound: Weighing the Contributions of Touch, Sight, and Balance
Bibliographic record
Abstract
Ludwig van Beethoven suffered many hardships in his life, but the least known among them may be the persistent slivers he endured while handling wood. Yes, wood! Be it through clenching a wooden stick between his teeth or cutting the legs off of a grand piano, he is said to have developed resourceful methods that enabled him to feel mechanical vibrations of music in an effort to compensate for his failing sense of hearing (Wallace, 2018). Indeed, by all accounts, Beethoven was profoundly deaf by the time he composed his masterful ninth symphony. Stories of its debut in Vienna in 1824 suggest that Beethoven had to be turned around to see the rapturous applause of the audience. The notion of feeling music continues to this day (Moore, 2019). Cities around the world host Deaf raves—giant parties where dancers feel the music through powerful subwoofers and bass shakers connected to floorboards. They also dance to the music, taking inspiration from visualizations that are projected onto large overhead screens. There is also a growing cadre of deaf musicians who are performing signed music (see bit.ly/37sQv4h). This music tends to be beat heavy, featuring lyrics delivered through sign language. Fueled in part by this cultural interest, researchers have begun to investigate the processes that enable deaf music and the ways in which auditory and nonauditory modalities combine to influence the experience of music for listeners of varying hearing ability
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".