Bibliographic record
Abstract
This article explores possible strategies for appraising electroacoustic and computer music to enhance ‘marketability’. It is proposed that the specific aesthetics, characteristics and function of a work may be more salient features than those of the medium of composition (e.g. computer) to many listeners. It is suggested that the common practice of focusing on chronology, geography and specific schools is becoming less relevant due to a proliferation of home studios, the internet, and an increasing saturation of electronic sounds in new media contexts. On the other hand, aspects of form, mood, timbral palette, rhythmic complexity, etc., may become very useful bases for choosing works for a compilation CD or concert programme. The inadequacies of musicians' discourse for describing such attributes leads to the incorporation of analogies from visual and performing arts as well as a discussion of other possible approaches to ‘labelling’ and the inherent dangers in such a task. In conclusion, it is proposed that the time is ripe for shuffling the categories and regrouping composers' works according to aesthetic preferences, regardless of the percentage of electronic/computer content.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".