Didactique de la littérature et littérature didactique: l’exemple de la littérature africaine francophone en classe de langues étrangères au Burkina Faso
Bibliographic record
Abstract
The teaching of foreign and second languages such as English, French and German in Burkina Faso has always used literature. However, its exploitation has always been limited to the handling and knowledge of the language. The potentialities of literary texts go beyond linguistic aspects. The didactic of literature helps to discover the potentialities of literature. They transmit cultural, socio-cultural, intercultural, empathic and educational skills. The purpose of this paper is to show not only how to teach literature in language class but also how to use didactic literature to develop social skills among learners to reduce vandalism, school violence, negative stereotypes, misunderstandings of intercultural nature while cultivating social peace and living together in a context of internationalization, globalization and digital revolution. Focusing on francophone African literature, the methodological approach used for the didactic transpositions of literary content is based on the new orientation and the redefinition of the objectives of language teaching according to the Common European Framework of Reference for Languages (2001) and on the didactic models of the aesthetic reception of didactics of literature from Bredella and Burwitz-Metzler’s (2004), which consists to put the learner in intensive interaction with the text and to motivate him to express oneself on one’s reading experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".