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Record W4393208554 · doi:10.1386/jpme_00128_1

The disconnected keyboard: Inclusive learning and musicking practice with modular synthesis

2023· article· en· W4393208554 on OpenAlexaff
Jason Nolan, Stefan Sunandan Honisch

Bibliographic record

VenueJournal of Popular Music Education · 2023
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of British ColumbiaToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsModular designComputer scienceCommunicationPsychologyHuman–computer interactionProgramming language

Abstract

fetched live from OpenAlex

Since the 1960s, electronic sound synthesis and the keyboard interface have been so closely associated that many young musicians have come to see them as inseparable components, if not interchangeable terms. In this article, we ‘disconnect the keyboard’ and explore an alternative history of electronic sound synthesis – modular synthesis – that has remained largely overshadowed by keyboard-based synthesizers since the Minimoog. Researchers in music education signal that Eurocentric aesthetic norms, ableist performance ideals and exclusionary practices are interwoven in keyboard technologies, creating barriers that extend into popular music education. Drawing upon critical discussions in music education and science and technology studies (STS), we examine the underexplored opportunities of using modular synthesizers for music learning. We examine how modular synthesis, liberated from the keyboard-controller, serves as a basis for exploring an alternative model for sound-based inquiry and for rethinking the possibilities of instrument design and ways of musiking that are more inclusive.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2023
Admission routes1
Has abstractyes

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