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Record W2905122763 · doi:10.4000/volume.5866

Mark Butler, Playing with Something that Runs. Technology, Improvisation, and Composition in DJ and Laptop Performance

2018· article· en· W2905122763 on OpenAlexaff
Emmanuel Parent

Bibliographic record

VenueVolume ! · 2018
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsArtImprovisationHumanitiesElectronic dance musicLaptopArt historyDanceVisual artsComputer science

Abstract

fetched live from OpenAlex

rayonnages d'une librairie.Si le noiser est celle ou celui qui fait face, comme l'affirment les auteures, à une indétermination essentielle du concret, alors se pose la question du discours adéquat.De quel ethos doit se munir la systématicité du discours, pour que son pouvoir régulateur et son autorité puissent entrer en adéquation avec la problématique de la noise ?Dans un tout autre contexte, Jean Cavaillès disait de l'empirisme qu'il « manifeste son essentielle fragilité dans l'imprévisibilité de ses caractères, dans l'illégal en quelque sorte qu'il comporte en lui » (Cavaillès, 1997 : 19).Se pourrait-il que ce soit de sa dimension empirique, de sa fragilité radicale, que le discours (et avec lui tout le dispositif académique) tire sa raison d'être et même sa légitimité ?Un autre enjeu ressortirait alors de cette étude.Il ne s'agirait pas seulement de considérer la place du discours dans la harsh noise, ni même d'évaluer l'apport singulier de la philosophie.La question posée par les discours pluriels qui entourent la harsh noise serait plutôt celle de la possibilité d'être en adéquation -de ses conditions mêmespour toute formation discursive s'intéressant à une pratique qui met en cause, de manière fondamentale, les normes établies de l'expérience. Bibliographie

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.003

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2018
Admission routes1
Has abstractyes

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