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Record W2597176154 · doi:10.7202/1038944ar

Musiques et identités remixées

2017· article· fr· W2597176154 on OpenAlexvenueno aff
G. O. Cote

Bibliographic record

VenueIntersections Canadian Journal of Music · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article présente une analyse contextuelle du travail créatif de quatre musiciens qui avaient comme mandat de remixer des chansons québécoises enregistrées avant les années 1960. Par le biais d’une enquête dont l’approche s’inspire de méthodes ethnographiques, l’auteur tente d’entrer dans la tête et le coeur des musiciens en vue d’identifier ce qui motive leur créativité et ce qui mène ces derniers à s’incliner vers certains modèles esthétiques plutôt que d’autres. Dans un espace social qualifié de postmoderne ou encore d’hypermoderne selon les sociologues, les motivations nous apparaissent diversifiées, fragmentées, fluctuantes et très contrastées d’un musicien à l’autre, et ce, à l’image d’une époque, d’une génération ou d’une réalité anthropologique urbaine et contemporaine. L’auteur conçoit la musique comme un phénomène multifactoriel où le social et le musical sont interdépendants, en mouvements ainsi qu’en constantes mutations.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.929
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0090.019
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.316
GPT teacher head0.471
Teacher spread0.155 · 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
Published2017
Admission routes1
Has abstractyes

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