Part of Computer Music History… (Trust Me, Latin America Has Always Been There!)
Bibliographic record
Abstract
Abstract The political and economic instability in most Latin American countries has been profoundly affecting the lives of its inhabitants for decades. Support for artistic activities has usually been postponed to solve urgent social problems. Despite that, the development in these countries of the electronic arts, in general, and electroacoustic and computer music, in particular, is astounding. Mauricio Kagel, Reginaldo Carvalho, Hilda Dianda, Juan Amenabar, Horacio Vaggione, Jorge Antunes, Jocy de Oliveira, José Vicente Asuar, and Juan Blanco are only some of the many names in the ocean of electroacoustic music creativity that has always been Latin America. Archiving and disseminating electronic art—and working on a revised version of its history—is crucial to comprehend the present and build our future. The Latin American Electroacoustic Music Collection, hosted by the Daniel Langlois Foundation for Art, Science, and Technology in Montreal, has over 1,700 digital recordings of compositions created between 1957 and 2007 by almost 400 composers. The Collection has recovered and made visible (and listenable) the creative work of many composers otherwise almost forgotten. It has defied the hegemony of the computer and electroacoustic music history narrative, helping to break barriers and widening the way their history is understood.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.024 |
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".