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Record W2896699241 · doi:10.29173/inton25

The BlipVert Method

2018· article· en· W2896699241 on OpenAlexaffvenue
William Rogers Northlich

Bibliographic record

VenueIntonations · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImprovisationElectronic musicStudioComposition (language)Style (visual arts)MusicalMusical compositionEthosCreativityVisual artsPopular musicComputer musicComputer scienceMultimediaArtLiteraturePsychologyLinguistics

Abstract

fetched live from OpenAlex

The confluence of composition and performance is a compelling phenomenon which confronts many 21st century electronic music artists, brought about primarily through an independent “DiY” ethos to creativity and the ubiquity of advanced musical, and non-musical, technology. Techniques of software programming, improvisation, reconstitution of electric and acoustic instruments, sampling, and manipulation of audio in a live setting (to name a few) may all find a place in an artist’s methodology regardless of style. It may be even be said that the techniques employed by an artist delineate the style itself, e.g. “controllerism,” “turntablism,” “live PA,” etc. The following paper offers an in-depth structural analysis of the composition and performance fundamentals of BlipVert, a pseudonym under which I have been presenting electronic music to the general public for almost two decades. The BlipVert composition “New Choomish,” from BlipVert’s 2010 release “Quantumbuster Now” (Eat Concrete Records, NL), is examined as a construct which manifests an expressive faculty in both live and studio environments, consequently demonstrating a profoundly synthesized framework of sonic and gestural principles.
 Keywords: composition, performance, improvisation, movement, building-blocks, Northlich, BlipVert, New Choomish

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.177
GPT teacher head0.312
Teacher spread0.134 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes2
Has abstractyes

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