Une image et sa musique : analyse de leur relation et de leur processus d’association
Bibliographic record
Abstract
Ce mémoire réunit les projets musicaux associés à l’image auxquels j’ai participé dans le cadre de mes études de deuxième cycle effectuées à la Faculté de Musique de l’Université de Montréal ainsi qu’au Conservatoire National Supérieur de Musique et de Danse de Lyon. La musique qui sera présentée, a été composée pour œuvres cinématographiques, jeux vidéo et pour la scène. Elle abordera les points suivants : - La relation entre la musique et l’image en lien avec les éléments thématiques musicaux. - Le processus de composition et d’association à l’image. - Le rapport à l’image en concert. L’analyse de ces œuvres permettra d’étudier plus en profondeur les rapports que j’établis entre musique et images, tant aux niveaux structurel et émotionnel qu’au niveau de leur synchronisation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".