<i>La Grande Séduction</i> pour le français parlé québécois : comment ne pas manquer le bateau
Bibliographic record
Abstract
Dans la classe de français langue seconde, le recours à des documents authentiques s’impose désormais. Le film, par exemple, commande de la part de l’enseignant une «pédagogisation» de l’information, un réel besoin de procurer, en classe, des explications et des extraits pour exemplifier l’écart présent entre l’oral et l’écrit, voire les couleurs locales de la langue orale véhiculée. L’apprenant peut dès lors s’immerger dans la langue orale quotidienne et dans ses variétés, saisir que ses couleurs se manifestent dans la richesse du vocabulaire, dans les expressions, dans la prononciation ainsi que dans les référents culturels. À partir d’un extrait du film La grande séduction, le présent article offre un cadre de réflexion et d’action où sont illustrées des particularités du français oral ainsi que des variations phonétiques et lexicales; où est exploitée la compréhension orale en débit courant du français parlé québécois familier. Mots clés : Documents authentiques, film québécois, compréhension orale, français parlé québécois, variations, registres de langue, compétence sociolinguistique. Abstract In the French-as-a-second-language classroom, authentic materials take an important place in the teaching process. Using a movie, for example, requires instructors to scaffold teaching/learning activities in order to make the target language accessible to learners. In the case of using a popular mainstream movie, these activities aim to highlight differences between oral and written language with a view to helping learners appreciate the nuances and richness of “everyday” French spoken in Quebec. These nuances go beyond vocabulary and extend to idiomatic expressions, pronunciation, and cultural referents. In this article, we first describe how an excerpt from the Quebec-made film La Grande Séduction is used as a vehicle for pedagogical reflection. Then, we discuss practical applications of aural/oral activities inspired by “everyday” spoken Quebecois French that draw learners’ attention to the various and distinctive features of the oral language. Key Words: Authentic materials, Quebec-made film, oral comprehension, spoken Quebecois French, language variations, register, sociolinguistic competence
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".