MusAIcology: AI Music and the Need for a New Kind of Music Studies
Bibliographic record
Abstract
As music generated using artificial intelligence ({\em AI music})becomes more prevalent -- originating not only from individualsbut also services or businesses centered around scalable content generation --the need to study it and its impacts grow.How can this material and its sources be meaningfully studied and critically engaged with, however?The paper begins to answer this principal question by considering six aspects of AI music,discussing each with reference to a contemporary AI music service: {\em Boomy.com}.The six aspects are: the service or company;the founders and employees;the use of the service;the users; the algorithms;and finally the resulting music.While our investigations are preliminary,they open several interesting avenues of explorationfor many disciplines and their intersections,motivating a new kind of music studies.Given the speed at whichAI music and associated services are developing, we post this paper to {\em SocArXiv}and move on to establishing a community defining and exploring this new kind of music studiesvia organizing {\em The First International Conference in AI Music Studies:Prospects, Challenges and Methodologies of Studying AI Music in the Humanities}.\footnote{\url{https://boblsturm.github.io/aimusicstudies2024}}
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".