La Littérature dans la classe de FLE: quelle utilité) et quelle portée?
Bibliographic record
Abstract
Etudier la litterature francophone en milieu hispanophone en Amerique Centrale est une veritable gageure parce qu’a l’exception d’une minorite, les costariciens n’ont pas l’habitude de lire. En outre les plus proches voisins francophones, le Canada et les Antilles francaises, sont assez eloignes.Cependant la litterature peut constituer un outil pedagogique tres utile. Son utilisation en classe FLE a une triple fonction : ameliorer les competences communicatives des etudiants, aborder la diversite culturelle et introduire l’etude de la litterature francophone. L’etude de certains romans maghrebins et africains se revele tres performante de par les techniques utilisees par les ecrivains : l’oralite, la serie narrative ou differents points de focalisation grâce auxquels chaque etudiant peut s’exercer aux differents modes discursifs. Les œuvres litteraires francophones peuvent aussi etre abordees par l’etude des strategies narratives qui les caracterisent. Pour faire comprendre la construction parfois extremement complexe de certains textes un « puzzle litteraire » a ete mis au point. C’est une activite ludique qui facilite la comprehension des courbes narratives structurant les recits et par la meme occasion les structures mentales et les aspects culturels des peuples dont ils ont issus.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".