Bibliographic record
Abstract
When first defined by Guido Adler in 1885 in his pioneering article on the new discipline of musicology, systematic musicology appeared simply to be a good idea: after all, since music operates and is created across a range of different contexts, an articulated awareness of the various methods that could be applied to its study seemed necessary to any definition of musicology. But humanistic tradition and institutional politics intervened, and musicological studies became increasingly divided into the historical and the ethnological, with no room left for anything else. The limits of Anglophone musicology as established in the 1930s, and the unfortunate tendency of academic institutions to reproduce their own methodological patterns, created a situation where entire areas of musical study are now considered peripheral or even dubious by most musicologists.This presentation will consider various definitions and studies of systematic musicology in the context of a number of related disciplines as they have been constituted in academe, as well as in the light of Kerman’s 1985 critique of the possibilities and limitations of our field. After examining some of the charts presenting the possible fields of musical scholarship, I offer my own chart of the potential disciplines of systematic musicology. All of these methodological divisions are invoked to recall those aspects of music that are denied by an exclusive focus on history and ethnology, as it reaches into the realms of the physical, the psychosocial, and the abstract.
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.
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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.133 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".