De temps à autre and espaces éphémères, for large orchestra: towards a spatialization of musical time
Bibliographic record
Abstract
Analysis dynamics (i.e.creating an impression of varying proximity through multilayered dynamic envelopes); harmony (creating depth or perspective by adding combination tones to harmonic aggregates). Background theoryThe present chapter will review recent literature that addresses the phenomenology of musical experience, particularly, the listener's assimilation of musical material on both local and global levels.In subsequent chapters, I will then discuss how this knowledge is incorporated into my compositional approach.For musicologist Philippe Lalitte, over long temporal spans memory is important for the listener's understanding of auditory information in musical experiences.7 Lalitte asserts that, on the local level, the listener's methods of processing musical data are "automatic and implicit", whereas a deliberate effort is required on the listener's part to assimilate information at the global level.For musicologist Michel Imberty, the processing of musical material on the global "macrostructure" level is fundamental to the listener's understanding of music.8 This processing involves both conscious and unconscious establishment of hierarchies by combining two distinct modes of perception of musical time: schemes of order, which refers to the listener's means of perceiving music as a succession of events; and schemes of relation of order, which refers to the listener's ability to develop an understanding of the formal organization of those events.For cognitive scientist Irène 7 Lalitte, Philippe, 'La forme musicale au regard des sciences cognitives', In: Reynal P. (Eds.), Structure et forme : du créateur au médiateur (pp.67-82).
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".