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Record W2546726017 · doi:10.15845/voices.v16i3.834

Improvisation, Adaptability, and Collaboration: Using AUMI in Community Music Therapy

2016· article· en· W2546726017 on OpenAlexaff
Mark Finch, Susan Quinn, Ellen Waterman

Bibliographic record

VenueVoices A World Forum for Music Therapy · 2016
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsJaneway Children's Health and Rehabilitation CentreMemorial University of Newfoundland
Fundersnot available
KeywordsImprovisationAdaptabilityMusic therapyAdaptation (eye)Computer scienceHuman–computer interactionFlexibility (engineering)Context (archaeology)MusicalPsychologyMultimediaVisual artsArtPsychotherapist

Abstract

fetched live from OpenAlex

Adaptive Use Musical Instrument (AUMI) is a digital instrument that facilitates independent music making for people with diverse ranges of mobility. Employing the camera tracking capabilities available on most digital devices, users with even very little voluntary mobility are able to create and perform music by controlling a visual cursor within adaptable parameters to trigger sounds. Instead of requiring players to conform to an instrument, AUMI’s flexibility enables it to adapt to divergent artistic impulses and individual bodies. Building on previous studies that examined AUMI in an educational setting (Oliveros et al. 2011) this article presents three case studies that explore AUMI’s use in a community music therapy context. In addition to assessing the instrument’s effectiveness in achieving specific music therapy goals, ethnographic research methods illuminated various socio-cultural implications of integrating digital instruments into a music therapy setting that challenge conventional notions of youth culture, independence, and collaboration. We conclude with a discussion of the notions of adaptability and universal design as they apply not only to AUMI’s functionality in the music therapy sessions, but also in view of the instrument's ongoing development.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.370
Teacher spread0.259 · 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.

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

Citations8
Published2016
Admission routes1
Has abstractyes

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