Relier l’art et le français à travers les émotions, l’intericonicité et l’expérience esthétique en classe de langue : une expérience menée avec des fonctionnaires du gouvernement fédéral du Canada
Bibliographic record
Abstract
This study investigates the use of painting images in the French as a Second Language (FSL) classroom to foster written and oral expression and to explore the emotions of beginner and intermediate learners. These linguistic and emotional competencies could be further developed during future cultural visits to museums. The central hypothesis is that this medium can stimulate learners to express their emotions in French and engage in classroom interactions. To this end, the research was conducted during online French course sessions intended for Canadian civil servants. The research methodology employed is multifaceted: initially, surveys were administered during the course at the beginning of the project to profile learners and instructors and to gather their perspectives on learning French through art and emotions. Subsequently, various oral recordings and written productions in French, as well as learners' commentaries on the artworks presented in class, were analyzed. We examined the learners' discourses before and after integrating emotional considerations using aesthetic experience and intericonicity. Our findings indicate that painting, beyond serving as an instrumental tool for speech production, is a rich source of emotions, history, and culture. Moreover, it promotes the development of soft skills, where aesthetic experience and intericonicity play a significant role, complementing the pragmatic and communicative objectives of French courses designed for civil servant learners in Canada
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".