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Record W4299305798 · doi:10.7202/1087838ar

Who’s Afraid of the Big Bad World Music ?

2000· article· en· W4299305798 on OpenAlexvenueno aff
Denis‐Constant Martin

Bibliographic record

VenueEthnologies · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Apparues quelque part aux débuts des années 1980, les musiques du monde, ou world music, correspondent à un ensemble hétérogène qui renferme tout ce qu’on ne peut classer dans les autres champs musicaux. Leur succès peut être vu comme une manifestation du désir de monde et de l’envie de l’Autre qui s’observent également ailleurs. Consommer des musiques du monde serait une façon d’exprimer le souhait d’un monde meilleur et le rêve de la réconciliation humaine. L’analyse de la musique permet de démontrer le caractère hétéroclite de cet ensemble, en même temps qu'elle met en lumière l’étroitesse des liens qui unissent musiques du monde et musiques commerciales modernes. Si les ethnomusicologues et autres collecteurs ont enregistré depuis longtemps des musiques populaires exotiques, la world music, elle, les donne à entendre au plus grand nombre, profitant ainsi commercialement de la soif d’exotisme. Les conditions techniques et les règles de la production ainsi que celles de la mise en marché sont également intégrées à l’analyse, puisqu’elles déterminent en partie ce que sont les musiques du monde.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.010
Scholarly communication0.0190.015
Open science0.0010.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0270.008

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 head0.080
GPT teacher head0.244
Teacher spread0.164 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2000
Admission routes1
Has abstractyes

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