Les collections d'instruments de musique. 1ère partie.: Musique-Images-Instruments. Revue française d'organologie et d'iconographie musicale. Volume 8.
Bibliographic record
Abstract
Deux volumes de Musique-Images-Instruments portent sur l'Histoire des collections d'instruments de musique de la Renaissance au XXe siècle. Cette thématique est consacrée aux collections disparues ou faisant partie aujourd'hui d'ensembles appartenant à des particuliers ou à des institutions. Elle traite aussi bien des cabinets de curiosité, des collections d'étude, d'instrumentarium liés à la pratique, que de laboratoires d'expérimentation ou de collections de voyageurs. Soulignant des aspects aussi variés que l'histoire du goût, le mécénat, l'histoire institutionnelle, le contexte culturel et les enjeux symboliques, ces études dessinent une anthropologie historique des collections et soulignent l'évolution de la notion de patrimoine. Ce volume 8, consacré aux collections des XVIIe et XVIIIe siècles, constitue la première partie de cette histoire des collections. Eszter Fontana, Musical Instruments for the Electoral Kunstkammer in Dresden around 1600 ; Florence Gétreau, Quelques cabinets d'instruments en France au temps des rois Bourbons ; Thomas Vernet, Les collections musicales des princes de Conti ; François Picard, Joseph-Marie Amiot, jésuite français à Pékin, et le cabinet de curiosités de Bertin ; Cristina Ghirardini, Les instruments chinois dans le 'Gabinetto Armonico' (1723) de Filippo Bonnani ; Caroline Giron, Une colleciton perdue : les instruments de l'ospedale des Mendicanti, à Venise ; Nicole Lallement, Inventaire des tableaux à sujets musicaux du musée du Louvre (VI) : suite et fin.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".