Bibliographic record
Abstract
In some informal remarks I made at a conference in 1979, I expressed a reluctance to deal with the subject of aesthetics, which historically is a product of European philosophy and which remains a troublesome concept for contemporary music where an aesthetic term such as ‘beauty’ seems to be studiously ignored (Truax 1980). In a recent, also informal article (Truax 1999) addressed as a ‘letter to a twenty-five-year old electroacoustic composer’, I predicted that the term ‘computer music’ would probably disappear since in an age where the computer is involved in nearly all electroacoustic music production, this term, which once distinguished a type of music from that made with analogue, electronic equipment, seemed today to be impossible to define rigorously. Therefore, the concept of the ‘aesthetics of computer music’, proposed as a panel discussion topic, initially seemed to me to be doubly suspect as to its meaning.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.077 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".