Teaching Music Research Methodology at the UndergraduateLevel: An Approach Developed at Memorial University of Newfoundland
Bibliographic record
Abstract
While it is usually possible to provide a modicum of research training at the undergraduate level in survey music-history courses, it is often difficult to provide more advanced training in smaller programmes.Even when a music-history major option is available to students, staffing problems will frequently prevent offering a dedicated upperlevel course in research methodology for history majors.The need for such training for music-history majors is real, however, especially for those contemplating graduate study.This was a problem which I faced in the School of Music at Memorial University of Newfoundland several years ago when I was the only musicologist in a programme serving some 150 students.If an upper-level course was to be offered to serve the needs of the relatively small number of history majors, it also had to be available (and attractive) as an elective course for all music students.
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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 | 0.056 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".