Bibliographic record
Abstract
The author is interested in the process of composing, i.e. the invention of a piece of music from the initial spark of intuition to the final product. His study is based on the “Germinal” project, an experiment in composition involving fourteen composers working with identical technical resources in computer studio 123 of the grm (Groupes de recherches musicales) in Paris in 1985. Their creations all followed a common work schedule in four stages, with each composer adopting a very personal strategy at each of these stages. Describing their strategies amounts to giving an account not only of their actions, but also of the attitudes and decisions that led from an initial project to a final product. Four levels of criteria governing the decision-making process are then proposed: technical quality, grammaticality, the musical idea and the topic. The first two conform to a search for regularity while the latter two suggest a desire for singularity. These levels determine work attitudes in relation to sound and machines, i.e., poietic attitudes. From this comparative study several strategies have emerged, three of which are explained. Finally, the author develops the concept of singularity, which is a central concern of composers, and which may also be viewed as a challenge to computers and music analysis.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".