Doctor Who? A Demographic Profile of Doctoral Recipients in Music From 1984 to 2022
Bibliographic record
Abstract
The purpose of this study was to examine the demographic profile of doctoral music recipients by discipline from 1984 to 2022. Using sociological institutionalist and feminist institutionalist frameworks, I analyzed institution-level panel data from the Integrated Postsecondary Education Data System ( N = 3,461) to examine the demographic characteristics of doctoral recipients in music education, music history/musicology, music theory/composition, and music performance. The number of doctoral completers in education, history/musicology, and theory/composition remained relatively stable over 4 decades. The number of doctoral completers in performance increased nearly fourfold, from 342 in 1984 to 1,253 in 2022. Compared to doctoral recipients across all academic disciplines, more music doctoral completers tended to be White. Music education and history/musicology recipients mirrored broader trends toward higher proportions of female doctoral recipients, but performance and theory/composition remained disproportionately male. Additionally, no music doctorates were ever awarded at Historically Black Colleges and Universities (HBCUs) despite the proportion of doctoral recipients at HBCUs in other disciplines increasing over the observed period. Results are discussed in the context of the formal and informal institutions that contribute to the homogenization of various music student and teacher populations across race/ethnicity and gender.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".