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Record W7124282822 · doi:10.4000/15ibu

Fanm d’Ayiti. La chanson haïtienne au féminin en diaspora

2023· article· fr· W7124282822 on OpenAlex
Sara Del Rossi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOltreoceano · 2023
Typearticle
Languagefr
FieldArts and Humanities
TopicCaribbean and African Literature and Culture
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsDiasporaContext (archaeology)Game studiesExoticism

Abstract

fetched live from OpenAlex

La musique est depuis toujours l’un des plus prolifiques moyens de compréhension interculturelle. Dans le contexte diasporique, elle permet aussi la construction et le renforcement d’un espace social et identitaire où tout immigré peut aisément (re)trouver les liens avec son pays natal. Cet article observe le rôle de raccord de la musique haïtienne entre les Haïtiens du “dedans” (en Haïti) et “du dehors” (en diaspora), mais aussi entre la communauté diasporique et le pays d’accueil (la France, les États-Unis et le Canada), à travers l’étude des parcours artistiques de Martha Jean-Claude, Toto Bissainthe, Mélissa Laveaux et Nathalie Joachim. Pour ces chanteuses d’origine haïtienne, la musique s’avère être une ressource de militance, de résistance identitaire et collective contre la démonisation d’Haïti, mais aussi une forme de dialogue avec les autres cultures du continent américain. L’analyse des divers effets d’hybridation musicale apportés au répertoire traditionnel, qui leur ont permis de rejoindre un public international, démontre leur capacité d’adaptation aux différents contextes de diffusion, mais aussi d’établir des po(i)nts de convergence interculturels inédits.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.533
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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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