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Record W2970782418 · doi:10.7202/1062568ar

Playing with Shadows: Reinjection Loops and Historical Allusion in Georg Friedrich Haas’s Live Electronic Music

2019· article· en· W2970782418 on OpenAlexvenueno aff
Landon Morrison

Bibliographic record

VenueCircuit Musiques contemporaines · 2019
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntonation (linguistics)ScholarshipElectronic musicString (physics)Tone (literature)AllusionLiteratureArtHistoryLinguisticsPhilosophyVisual artsPhysicsTheoretical physicsLaw

Abstract

fetched live from OpenAlex

Recent scholarship suggests that the music of Austrian composer Georg Friedrich Haas can be understood as a dramatic confrontation between “clashing harmonic systems” (Hasegawa, 2015). Building on this observation, the present article focuses on Haas’s recent endeavors in the genre of live electronic music, showing how the composer deploys a relatively straightforward technical procedure—the reinjection loop,orthe delayed playback of recorded sound at various speeds—to juxtapose different modes of pitch organization, including twelve-tone equal temperament, ultrachromatic microtonality, and just intonation. After surveying the composer’s relationship to these historical idioms, the article presents detailed analyses of two pieces (Ein Schattenspieland String Quartet No. 4) to illustrate how the reinjection loop operates at multiple registers simultaneously, bringing the performers into contact with their immediate past, while also bringing Haas into dialogue with the shadows of his own compositional predecessors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.013
Scholarly communication0.0050.004
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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