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Record W2531990716

Keïta ! L’héritage du griot, de Dani Kouyaté: la transmission comme technique narrative?

2016· article· fr· W2531990716 on OpenAlexvenueno aff
Elhadji Moustapha Diop

Bibliographic record

VenueMouvances Francophones · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Dans quelle mesure peut-on envisager la ‘tradition’ orale, sous l’angle de la translatio, non comme source originelle, mais facteur de proliferation, non comme matrice de signes et de sens, mais point d’indetermination ou il est un peu fastidieux de demeler perspectives artistiques et approches historiographiques, d’activer un jeu de distinctions entre ce qui releve de la « piece historique » et de la « these d’histoire » ? Les deux niveaux identifies dans le processus d’adaptation litteraire, a savoir le performanciel et l’infra-discursif, permettent de suivre le cours d’une traversee des signes ou les modalites de l’enonciation et la transvaluation des codes (discursifs, narratifs, esthetiques) jouent un role de premier plan. En va-t-il tout autrement, dans le cas d’une œuvre filmique ? Comme on va le voir avec Keita ! L’heritage du griot (1994) de Dani Kouyate, au cinema la logique du transfert intermedial ne saurait se soustraire aux exigences du dispositif de mise en images. En tirant argument de certains travaux sur le reformatage et la « reutilisation » des codes, ainsi que sur la « releve » du support technique, on s’efforcera de montrer dans cette etude que la reappropriation de la voix du griot, telle qu’elle est a l’œuvre dans ce film, est fortement tributaire d’une forme oralede litterarite, d’ou la recurrence d’une structure en abyme, sur laquelle insiste particulierement Valerie Thiers-Thiam dans A Chacun son griot.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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