Advancing interdisciplinary research in singing through the AIRS Test Battery of Singing Skills
Bibliographic record
Abstract
The four articles in this issue are all products of a major collaborative research initiative called Advancing Interdisciplinary Research in Singing (AIRS).1 The AIRS project, directed by Annabel Cohen, received funding for seven years from the Social Sciences and Humanities Research Council of Canada (SSHRC), starting in 2009, encouraging singing research across disciplines and cultures. There are three primary research themes focusing respectively on singing and development, singing and education, and singing and wellbeing, each of which has three subthemes. In aiming to achieve the goals of each of these sub-themes, AIRS has brought together international scholars in research on singing and funded students in a wide range of projects. As in the case of the papers in this issue, the fruits of the AIRS collaboration have resulted in publications as well as theses, dissertations, and presentations. The pivot point for all of the articles in this issue is the AIRS Test Battery of Singing Skills (ATBSS), concerning only one out of the total of nine sub-themes of the AIRS project and falling under the primary heading of Development of Singing. The test battery is described thoroughly in the first paper, and there the rationale and purpose of the test battery are explained. The ATBSS contains 11 test items. There are seven distinct test items involving musical vocal production (singing), with the remainder testing verbal ability, singing range or making up a story. Six of these singing items are the subjects of the following papers. In the first paper by Annabel Cohen, the focus is on the singing of the familiar song “Brother John”. Pitch accuracy is analyzed for an extremely wide range of age groups (pre-school to octogenarian), and the sensitivity to the musical hierarchical structure is also commented on. The second paper by Beatriz Ilari and Assal Habibi analyzes the singing of a “favorite song” by young children in a cross-cultural study involving Brazilian and US-Latino children as well as the melodic element component which assesses the ability to sing back short sequences of notes (scalar passages or a triad) from 5 to 8 notes in length. In the third study by Marju Raju, Laura Valja, and Jaan Ross, improvised endings of songs by young Estonian children are analyzed, and the fourth study by Cohen, Bing-Yi Pan, Alexis McIver, and Leah Stevenson examines the effect of native language when learning a new song. This study compares Englishand Chinesespeaking university students. The findings of these papers are encouraging for further research 598735 MSX0010.1177/1029864915598735Musicae ScientiaeEditorial research-article2015
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".