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Record W2030498175 · doi:10.7238/a.v0i13.2000

The Latin American Electroacoustic Music Archive ... Ten Years on

2013· article· en· W2030498175 on OpenAlexaff
Ricardo Dal Farra

Bibliographic record

VenueArtnodes · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLibraries, Manuscripts, and Books
Canadian institutionsConcordia University
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVUniversidad Nacional del LitoralUniversidad Simón BolívarUniversidade de São Paulo
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

La creació musical amb mitjans electroacústics té una història llarga, interessant i prolífica a l'Amèrica Llatina. Molts dels compositors que han nascut o viscut a la regió van desenvolupar una destacada tasca en el camp de la música electroacústica, en alguns casos començant les seves activitats d'experimentació i creació fa cap a 60 anys. No obstant això, la possibilitat d'accedir a enregistraments i informació relativa a aquest àmbit ha estat sempre difícil, tant per a educadors, compositors, intèrprets, investigadors i estudiants com per al públic en general. En un esforç per a preservar, documentar i difondre almenys una part de la creació musical feta amb mitjans electroacústics per compositors nascuts a l'Amèrica Llatina, o clarament vinculats amb aquesta regió, es va crear un arxiu a la Fundació Daniel Langlois per a l'Art, la Ciència i la Tecnologia de Mont-real fa gairebé una dècada. Des de llavors és consultat àmpliament i ha facilitat la recuperació i el reconeixement de l'obra de compositors els treballs dels quals havien estat oblidats o perduts, i d'aquesta manera ha ajudat la memòria col·lectiva a valorar els assoliments i les dificultats dels qui ens van precedir, per a comprendre millor el present i pensar el nostre futur.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.144
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.012
Science and technology studies0.0030.001
Scholarly communication0.0090.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1440.060

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.

Opus teacher head0.099
GPT teacher head0.194
Teacher spread0.094 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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