Les nouvelles technologies à l’opéra. Trois oeuvres lyriques revisitent le genre opératique
Bibliographic record
Abstract
La présente étude vise à montrer dans quelle mesure l’utilisation des nouvelles technologies à l’opéra permet de revisiter le genre opératique en proposant une nouvelle hiérarchisation des paramètres qui le constituent, notamment les composantes auditives et visuelles, ainsi que la scénographie, le lieu de production et le mode d’écoute. Afin de démontrer cette nouvelle hiérarchisation, trois oeuvres récentes et novatrices sont examinées en guise d’études de cas : EROR (The Pianist) (2019), un opéra sans chant de Georgia Spiropoulos ; Invisible Cities (2013), un opéra pour écouteurs de Christopher Cerrone, de même que Laila (2020), une oeuvre interactive mise au point par Opera Beyond, un projet chapeauté par le Finnish National Opera. Cette analyse démontre comment les oeuvres parviennent à confronter les définitions sociales et musicologiques de l’opéra, sans pour autant faire émerger une nouvelle définition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".