Advancing Interdisciplinary Research in Singing through a Shared Digital Repository
Bibliographic record
Abstract
The domain of research in singing encompasses numerous disciplines, countless styles, and many lifespan stages of skill development. A comprehensive understanding of this domain would benefit from a vast digital repository for storing, accessing, and annotating recordings of singing in all its manifestations. A cross-cultural strategy for acquiring and storing the essential audio, audiovisual, and symbolic data parallels the Child Language Data Exchange System (CHILDES) developed and maintained by Brian MacWhinney (http://childes.psy.cmu.edu/) and used by hundreds of psycholinguistics researchers worldwide. A prototype of a digital repository for singing (http://vre.upei.ca/chimes) is under development using infrastructure at UPEI associated with a Canada Foundation for Innovation project on the enhancement of education through media. The database will support and be supported by an international team aiming to Advance Interdisciplinary Research in Singing (AIRS) on the following themes: (1) acquisition of singing (2) comparison of singing and speech acquisition (3) singing pedagogy (4) singing and intercultural understanding (5) singing and intergenerational understanding (6) singing and well-being. The breadth of the work of Johan Sundberg, an honoured member of this team, encompasses these themes and exemplifies the international, interdisciplinary scope of the initiative. (Supported by Canada Foundation for Innovation CFI).
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 | 0.048 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.029 | 0.039 |
| Open science | 0.005 | 0.043 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".