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Record W1907374615 · doi:10.4000/volume.3808

Consommer la musique à l’ère du numérique : vers une analyse des environnements sonores

2013· article· fr· W1907374615 on OpenAlexaff
Raphaël Nowak

Bibliographic record

VenueVolume ! · 2013
Typearticle
Languagefr
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Cette note de recherche porte sur l’analyse qualitative de la consommation musicale à l’ère du numérique. À travers l’examen des interactions entre les individus et la musique, j’argue que les analyses « écologiques » et « constructivistes » sont trop restreintes, en ce qu’elles ne tiennent pas compte de l’éclectisme grandissant des pratiques technologiques. En effet, les modes de consommation hétérogènes remettent la matérialité des supports d’écoute au centre des questions de réception de la musique. En conséquence, il convient d’introduire la matérialité des objets afin de comprendre les modes de consommation contemporains ainsi que les affects musicaux.

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)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.007
Scholarly communication0.0080.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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