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Record W4406910885 · doi:10.1080/09298215.2024.2442356

On mapping as a technoscientific practice in digital musical instruments

2024· article· en· W4406910885 on OpenAlex
Andrew McPherson, Landon Morrison, Matthew Davison, Marcelo M. Wanderley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of New Music Research · 2024
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
FundersUK Research and Innovation
KeywordsMusicalComputer scienceArtVisual arts

Abstract

fetched live from OpenAlex

This article provides historical context for the emergence of ‘mapping’ as a key conceptual metaphor in the context of digital musical instrument (DMI) design and use. In addition to a consideration of different technical implementations, we offer a critical assessment of the tendency to over-generalise mapping as a universal model for both building instruments and analysing them in retrospect. This reification of mapping as a design model, as well as of the dimension spaces of sound and gesture being mapped, is read through a media-theoretical lens, drawing on recent work from interface studies to show how mapping actively constructs ideological relationships between performers and underlying systems of musical representation. While acknowledging the practical utility of traditional formulations of mapping in DMIs, we focus on issues arising from their over-generalisation, including the sometimes-misleading impression of representational stability, the suitability of spatial metaphors, and the assumption of unidirectionality and temporal stasis. In closing, the article explores alternatives based on a relational approach to mapping as an ‘intra-active’ process that is bidirectional at every step, fluid in its distinction of categories, and more dynamic across its variegated temporalities.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.101
GPT teacher head0.392
Teacher spread0.290 · 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