Musicologie historique, ethnomusicologie, analyse: Une musicologie générale est-elle possible?
Bibliographic record
Abstract
In the spirit of Irène Deliège's work, notably through enabling a good number of special issues of Musicae Scientiae, towards dismantling the barriers between the various branches of musicology, the author examines some of the reasons behind the fragmentation of our discipline and proposes some ways of building a unified, general musicology. He attributes the fragmentation to ontological issues, to the great variety of phenomena which emerge or of which one takes account in the 20th century, and to the diversity of methods and epistemological conceptualisations to which musicologists subscribe. Seeking to reunify the discipline, he begins by defining common ground, and identifying opportunities for complementarity between historical musicology and the anthropology of music. Then he examines the role of music analysis in these two disciplines. Ultimately, he proposes to found musicology, whatever type of music it concerns, on the distinction between immanent structures, compositional and perceptual strategies, and contextual interpretations - historical, social and cultural. To do this, he reaffirms the need for research on musical universals, notably by systematic development of typologies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".