Enseigner le FLE avec deux œuvres portant deux regards sur la francophonie: le film Chocolat de Claire Denis et le roman Allah n’est pas obligé d’Ahmadou Kourouma
Bibliographic record
Abstract
Dans cet article, nous présentons l’utilisation de deux œuvres artistiques en classe de FLE qui nous ont permis de traiter de certaines formes de réalités interculturelles et d’altérité inhérentes à la Francophonie : une œuvre littéraire africaine d’expression française, Allah n’est pas obligé de l’écrivain ivoirien Ahmadou Kourouma, et une œuvre cinématographique, Chocolat de la cinéaste française Claire Denis. Nous proposons une démarche pédagogique de découverte et d’analyse du phénomène linguistique et culturel de la francophonie africaine au travers de ces deux œuvres afin de comprendre en partie son origine et aussi de savoir qui est l’Autre, dans son histoire, dans sa culture, dans sa langue (dans ses langues en ce qui concerne Kourouma). L’incursion dans le domaine de l’expression artistique visuelle par le cinéma et écrite par la littérature, donne à l’apprentissage de la langue une dimension sensorielle et émotionnelle complémentaire à l’aspect purement linguistique et culturel et apporte la touche humaniste nécessaire à toute pratique d’enseignement-apprentissage qui engage la construction de soi et les relations à autrui.
 
 Teaching French as a Foreign Language (FFL) with two artistic genres, two ways of looking at French-speaking countries. The colonizer and the colonized: Chocolat by Claire Denis and Allah n’est pas obligé by Ahmadou Kourouma
 In this paper, we will present the use of two works of art in a FFL class that will allow us to deal with intercultural realities and otherness inherent to French-speaking countries: an African literary work written in French, Allah n’est pas obligé by the Ivorian writer Ahmadou Kourouma, and a cinematographic work, Chocolat by the French movie-director Claire Denis.
 Through these two works, we will propose a pedagogical approach to discover and analyze the linguistic and cultural phenomena that African French-speaking countries represent nowadays in our teaching practice. This analysis will allow us to partly understand its origin and also to know who is the Other, in history, in culture, in language (in his languages as far as Kourouma is concerned).The incursion in the field of visual artistic expression through cinema and artistic written expression through literature gives a sensorial and emotional dimension complementary to the purely linguistic and cultural learning process and the humanistic touch necessary to any teaching / learning practice that involves self-construction and relationships with others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".