Bibliographic record
Abstract
La musique, c’est bien connu, est avant tout source d’émotion et de plaisir. Elle accompagne notre vie quotidienne, dont elle constitue le paysage sonore sans même que nous nous en rendions compte. Alors, pourquoi y a-t-il des musicologues, c’est-à-dire des chercheurs qui prétendent aborder la musique d’un point de vue scientifique ? Quelles questions se posent-ils ? Quels problèmes cherchent-ils à résoudre ? Comment travaillent-ils et où ? Le discours sur la musique ne serait-il pas quelque peu parasitaire ? Que peut-il apporter aux mélomanes et aux amoureux de la musique ? Telles sont quelques-unes des questions que le grand public se pose souvent au sujet de la profession de musicologue, et auxquelles Jean-Jacques Nattiez tentera de répondre en empruntant des exemples concrets à ses propres champs de recherche : la musique de Wagner et celle… des Inuit.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.183 | 0.015 |
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; both teacher heads agree on what is shown here.
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".