Gaining Insights into Signed Music Through Performers
Bibliographic record
Abstract
Signed music is best described as an inter-performative art form that combines lyrical and non-lyrical musical performances and is deeply rooted in the culture of deaf people who communicate through signed language (J. H. Cripps & Lyonblum, 2017; J. H. Cripps et al., in press [a]). The key investigative component for this article includes outlining the experiences that three Canadian performers had about their signed music creativity during a plenary at the Partition/Ensemble 2020 Conference held by the Canadian Association for Theatre Research in Montreal, Quebec. The panelists responded to two questions that they developed for themselves: What inspired us to become musicians? How did the creative process of composing the signed music piece occur from the beginning to the end? The paper also covers an open discussion that the three performers had among themselves. Some signed music work examples are provided for viewing to support the premise that deaf people have full capacity for the creation and enjoyment of music. The paper represents a departure from the long-held view that music can only prevail in the audible form. The insights gained from the three deaf performers are the first of their kind and will contribute to the musical world.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".