Teaching French Language Through Films: The Cultural Contents in French and Francophone Films
Bibliographic record
Abstract
Foreign language teaching in the current globalization era needs to compete with technological development. This competition is related to the discovery of digital technology-based methods to motivate learners to provide more interesting cultural content in language classes. For teachers from different cultural backgrounds, authentic documents such as films are considered very effective in delivering cultural content. This research takes a French film, Intouchables, and a Canadian francophone film, Monsieur Lazhar, as a research corpus for their cultural content to be analyzed. The proper understanding of the two films' cultural content can inform a digital technology-based French-language teaching medium. To discover what strategies or formulas are used in teaching French with cultural-laden films as teaching media requires studying the films by dissecting the structure of the text. Examination of the structure of the films was based on the theory of Boggs and Petrie (2008), equipped with in-depth reading to find signs in the text by referring to Buckland (2004); and also the identification of cultural content using the cultural approach by Stern (1992). The construction of a teaching plan with a language teaching approach by Damen (1987) and Byram (1997) will be the last step. This research provides an academic outcome that is a film structural analysis to identify cultural content in the two films. The second outcome is a practical categorization of cultural content utilized as a language teaching material.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".