Does non-native language influence learning a melody? A comparison of native English and native Chinese university students on the AIRS Test Battery of Singing Skills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The AIRS Test Battery of Singing Skills (ATBSS) is a protocol for acquiring data about a wide range of singing abilities, one of which is the ability to learn an unfamiliar song. This test component presents a song with English lyrics. We examined the role of native language of the singer in this task by comparing the sung productions of the song by native English versus native Chinese students. Following a pilot study that suggested the impact of native language, in the main experiment, 12 participants (6 from each language group) heard the test song with lyrics in their native language (English/Mandarin Chinese), and 12 more heard the lyrics in the foreign language. The instructions were to sing “la” and not the lyrics. For both native-language groups, those who heard the song in their native language made fewer melodic contour errors. When later asked to sing the song with lyrics, the benefit of native language was also evident for both contour and lyrics. These effects of degree of matching of one’s native language with the language of the lyrics when learning a new song emphasize the importance of controlling for native language when recruiting for the ATBSS. It also validates efforts to create versions of the ATBSS in prominent world languages.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it