Bibliographic record
Abstract
Le langage corporel est souvent le parent pauvre en didactique du français langue étrangère que ce soit dans les recherches théoriques ou en classe de langue. Il constitue pourtant une sérieuse source d’interférences dans les situations de communication interculturelle. Cet article traite d’une approche originale du langage corporel en classe de français en s’appuyant sur les arts de la scène. La pratique du clown, du mime, la technique de déambulation en défilé de mode et quelques exercices de taiji quan (un art martial chinois) sont sollicités dans le cadre d’un cours visant à entrainer des apprenants de Taiwan à l’exercice du discours en public dans une perspective du langage corporel. Body Language and Cross-cultural Studies: A case study in Taiwan Body language is often neglected in theory and practice in the field of teaching French as a foreign language. This happens in spite of its importance in the many aspects of miscommunication between people of different cultures. This article deals with an original approach of body language by using various stage arts in the class of French. Exercises of clown, mime, catwalking and taiji quan (a Chinese martial art) are used to train students from Taiwan in the practice of oral discourse while focusing on body language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".