Music teaching and learning online: Considering YouTube instructional videos
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
This article is the initial foray into a long-term comprehensive collaborative investigation of online music teaching and learning. We considered representative YouTube videos (N=40) from five folk/traditional music websites for pedagogical and musical content. Video selection and categorization included banjo (n=10), fiddle (n=10), guitar (n=10) and mandolin (n=10) lessons. Content analysis factors took account of (1) video characteristics (length, teacher talktime), (2) instructor characteristics (gender, age, ethnicity), (3) musical content and (4) teaching methods. Results indicated that the majority of the selected videos were geared towards beginners and that instructors tended to be white, middle aged males. Videos also included many forms of aural reinforcement, modelling, technique-based instruction and physiological prompts. Opportunities for improvisation, however, were infrequent.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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