Bibliographic record
Abstract
Algorithmic composition, automated composition, meta-music, process and systems music, generative music, adaptive and procedural audio — all these terms indicate the extent to which composers have become conscious of system-building. Whilst the grand history of music attests that formalism is not a new idea of the computer era (Roads 1996, Loy 2007, Essl 2007), energetic exploration has certainly been facilitated by computers. Generative music itself is to some just a fashionable relabelling of realtime algorithmic composition, dating to publicity circulating around Brian Eno's work in the mid 1990s, particularly the 1996 Generative Music 1 installation/program release built with the Koan software. To others, generative music is a broad conceptual category within generative art. 0 Boden and Edmonds (2009) suggest that generative music might encompass any rule-based 0 0 system, no matter how subjective the rules, and thus take in Stockhausen’s Aus den sieben Tagen text pieces (1968). In a more contemporary sense, generative music is a subset of 0 computer-generated music (CG-music in their parlance) that requires the construction of objective programs which embody rules, and is thus strictly formal in the sense of being computable. In raising such distinctions, we see how the broad church of generative music
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".