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Record W3048122096 · doi:10.5817/cejcs2019-14-6

Voix identitaires et intimes dans Tout ce qu'on ne te dira pas, Mongo

2019· article· en· W3048122096 on OpenAlexaboutno aff
Georgeta Prada

Bibliographic record

VenueThe Central European journal of Canadian studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCaribbean and African Literature and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsGender studiesSociology

Abstract

fetched live from OpenAlex

Écrivain migrant franco-canadien contemporain d'une sensibilité particulière, Dany Laferrière représente le Passeur, celui qui relie à merveille le paradigme identitaire de la terre natale, Haïti, et celle d'accueil, le Québec. Nous allons analyser les préjugés et les traits du Sud et du Nord, en insistant également sur les règles à respecter pour arriver à une acculturation réussie, telles qu'elles apparaissent dans le roman Tout ce qu'on ne te dira pas, Mongo. Nous nous pencherons sur les thèmes de prédilection de l'auteur : le retour chez soi, l'identité, la situation des immigrants, l'exil. L'amour déclaré au Québec et l'humour raffiné s'harmonisent d'une manière touchante dans la chronique de Dany Laferrière, véritable écriture de l'intime, de l'autobiographie, de la mémoire. Conçu comme un dialogue entre un jeune Africain récemment arrivé au Québec et le narrateur, figure exemplaire du maître sage et expérimenté, le roman contient aussi des analyses critiques de la société québécoise contemporaine, des notes, des observations et des extraits de ses chroniques radiophoniques. Nous montrerons quelles stratégies utilise l'auteur dans cet ouvrage atypique où l'identitaire et l'intime fusionnent d'une façon sincère et spécifique à l'écrivain haïtien.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.961

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.210
Teacher spread0.187 · 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.

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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