L’enseignement et l’apprentissage de la littérature française en milieu universitaire hétéroglotte : pistes didactiques
Bibliographic record
Abstract
D’une part, les défis de l’enseignement des langues ont su réunir des chercheurs dans une démarche scientifique ayant permis l’émergence de la didactique des langues, elle-même plus tard divisée en différents volets; d’autre part, la littérature s’est elle aussi dotée de sa propre didactique. Cependant, l’enseignement de la littérature en milieu hétéroglotte demeure en marge dans le champ de la didactique. On part souvent du principe que les étudiants suivant un cours de littérature dans une deuxième ou une troisième langue ont des compétences langagières assez solides dans la langue cible pour relever les défis de la littérature, ce qui est rarement le cas. Cet article propose donc, en suivant les étapes de conception d’un cours de littérature française offert en deuxième année d’université, d’explorer les cheminements et les défis de ces deux domaines bien établis de la didactique et d’en montrer la complémentarité et la pertinence pour l’enseignement et l’apprentissage de la littérature en milieu hétéroglotte. On the one hand, the challenges of language teaching have brought together a community of researchers with the same scientific goal, which gave birth to the field of didacticism in languages, later divided into several subsets. On the other hand, the field of didacticism in literature has also emerged. However, teaching literature in a foreign or second language stills remains in the margins of didacticism. We often assume that students who take literature courses in their second or third language have strong enough language skills in the target language to rise to the challenges of literature, but it is rarely so. Therefore, while following the different stages of conception of a second year French literature course, this article proposes to explore the meanderings and challenges of these two well-established areas of didacticism and it will show their interrelation and their relevance to the teaching and learning of literature in a foreign or second language.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".