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Record W4385490630 · doi:10.7202/1102404ar

What Can Music Learning Do? Audiovision as Research-Creation in Undergraduate Music Studies

2023· article· en· W4385490630 on OpenAlex

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenuePerformance Matters · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsMusicologyDiversity (politics)SociologyVisual artsMusic educationAestheticsEpistemologyPedagogyArtPhilosophyAnthropology

Abstract

fetched live from OpenAlex

Livestreaming as research-creation for music studies introduced students to research-creation and the felt experience of extralinguistic concepts. As a way of both rethinking the divide between musicology and music performance and engaging in much needed critical reflection on how music teaching has always been done, research-creation in audiovision creates a laboratory for extralinguistic musicology. By connecting research-creation literature with practical training in the production of audiovision music studies, dominant image of thought emerges and a new machinic image of thought is introduced. If music studies is to find its way beyond the disciplinarity of inherited models, it will do so along with a wider engagement in a diversity of what it means to teach and what it means to do research. This is, at its core, a question of what image of thought will be allowed.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.240
GPT teacher head0.338
Teacher spread0.097 · 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