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Record W3082998960 · doi:10.1386/eme_00045_1

Interviewing the musical sample

2020· article· en· W3082998960 on OpenAlex
Sean Groten

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

VenueExplorations in Media Ecology · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMusicalSample (material)InterviewDigital audioMusic technologyImprovisationActive listeningCreativityHeuristicsEmbodied cognitionPsychologyVisual artsSociologyAestheticsComputer scienceArtCommunicationSocial psychologyMusic education

Abstract

fetched live from OpenAlex

Abstract Digital technologies and Musical Instrument Digital Interface-sampled instruments have emerged as one of the most significant technological shifts in musical consciousness in western society. Digital music has introduced new epistemologies of music as it raises questions of authorship and creativity, while also challenging the ontological presumptions about what it means to be a musician. Through interviewing the sample by applying various posthuman heuristics, I explore my own relationship to digital music samples and sampling technology as a composer and musician. I engage in a phenomenological inquiry that surveys the various ways the sample affects my ecological milieu of music-making, and more broadly, I explore how a musician is at all times enacting an intra/actional relationship as negotiated between themselves and their instrument.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.143
GPT teacher head0.252
Teacher spread0.108 · 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