A digital library to advance interdisciplinary research in singing
Bibliographic record
Abstract
In 2008, at the ASA/EAA symposium honouring pioneering scientist of singing, Johan Sundberg, the AIRS (Advancing Interdisciplinary Research in Singing) project was introduced as a major collaborative research initiative on singing [Cohen, A.J. (2008) Acoustics 08 Paris, 3177-3182]. Over 70 collaborators around the world were to investigate singing from perspectives of development, education and well-being. A digital library was to facilitate distant team members' work on the same data, such as examples from voice studios around the world, performance stages, playgrounds, public places, solos, groups, classrooms, intergenerational or multicultural choirs, therapeutic settings or new tests of singing skills [Vincent, C., Lane, J. & Cohen, A. (2011) PEVOC9]. Plans also included tools for annotation and analysis along with relevant documents and images. The present progress report on this endeavor describes preliminary prototypes, stages of development, and the current functional implementation. It is noted that although singing is primarily an acoustic and auditory phenomenon, video records of the singer are highly valuable. Their benefit, however, must be weighed against challenges arising from ethical considerations and storage requirements. Issues of ownership and data sharing are also raised as are practical matters of choice of platform, storage, formats, backup, human resources, and long-term preservation.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".