Bibliographic record
Abstract
Cheval mémoire (a piano drama) is a thirty-minute musical composition scored for solo piano, electronics, and six antique radios. In the broadest sense, the piece metaphorically dramatizes the processes of memory: how events, experiences, and information either transform into memories or forever vanish. To this end, two narrative elements push the action forward: a poetic text, which features a young woman recollecting the memory of her mother's departure; and a visual metaphor in which the piano, the pianist, the antique radios, and the audience symbolize the inside of a human head. The piece then alternates between sections where the pianist musically reacts to the poetic text heard from speakers surrounding the audience, and sections where the pianist performs various dramatic actions around the piano, including moving the radios from on top of the instrument to the stage floor. The sum of these actions symbolizes the creation and consolidation of memories. The composition's musical materials stem primarily from computer-assisted processes, including spectral analyses and various graphical derivations, applied to a series of recorded interviews on the topic of radio, which contrast with the poetic text. On a technical level, the piece explores the idea of live loudspeaker repositioning (i.e., physically moving speakers during a performance) to modify the perceived dimension of a sound source on stage; and also examines how synthesized piano resonance combined with other synthesized sounds can expand the registral and timbral possibilities of the live piano.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.007 |
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".