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Record W4286382213 · doi:10.3138/jeunesse.13.1.59

L’humour dans Les nouveaux contes d’Amadou Koumba de Birago Diop et La belle histoire de Leuk-le-lièvre de Léopold S. Senghor et Abdoulaye Sadji

2021· article· fr· W4286382213 on OpenAlexvenueno aff
Nene Diop

Bibliographic record

VenueJeunesse Young People Texts Cultures · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicAfrican Studies and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Au Sénégal, les contes ont une vocation ludique et didactique et constituent un discours collectif avec comme fonction de recueillir, de transmettre et d’assurer la sauvegarde de l’héritage culturel. L’humour dans les contes, objets de notre étude, constitue donc un outil de communication facilement accessible et disponible pour aborder certains thèmes considérés comme difficiles voire trop sérieux pour les enfants. Dans notre analyse nous démontrons que, pour Birago Diop, et Leopold S. Senghor et Abdoulaye Sadji, la satire, l’humour malicieux, les cousinages à plaisanterie et les jeux de mots ne sont que d’importants moyens, parmi d’autres, de raconter et d’illustrer la société et la complexité de ses réalités. De par leurs contes, ils ont su créer des oeuvres originales mettant en évidence et en valeur l’importance du mouvement de la Négritude et contribuer pertinemment aux efforts de réhabilitation et de redynamisation des cultures, des us et coutumes sénégalaises. Face aux défis de notre monde d’aujourd’hui empreint de tensions liées à des formes d’inégalités et de discriminations sociales à fortes connotations raciales, les messages et leçons de conduite, de morale et d’équité si généreusement légués par nos prolifiques auteurs ne peuvent être qu’appréciés.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.011
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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