A Snapshot of Music History Teaching to Undergraduate Music Majors, 2011–2012: Curricula, Methods, Assessment, and Objectives
Bibliographic record
Abstract
A survey of 232 music history teachers representing 204 institutions in the U.S. and Canada gathered descriptive data on the design, teaching methods, assessment and objectives for music history for undergraduate music majors in 2011–2012. On average, 8.5% (i.e. nine of 120 credits) of a typical music major’s degree is in music history. Students most often begin in the second year. The most common curriculum features only a chronological survey (n=81), while 37 add a one-course introduction and 21 add a menu of topics courses to the survey. Lecture is the most frequently used teaching method, but topics courses are more likely to use non-textbook readings, whole-group discussion, and guided listening than lecture. Examinations were the most significant mode of assessment by a large margin, followed by short writing assignments, participation/attendance, and fieldwork, oral histories, or interviews. Respondents preferred such traditional objectives as “trace the basic chronology of western art music” over objectives focusing on popular or world music, instruments, or performers, although cultural context was second on the list. Rankings of other objectives showed little variance. The author suggests that cultural changes and new research directions may challenge the traditional cast of music history teaching.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".