Interaction orale et production écrite : inférer pour mieux communiquer
Bibliographic record
Abstract
Cette contribution s’intéresse à la compétence stratégique chez les apprenants du français langue seconde (FLS) de niveau intermédiaire en contexte universitaire et en contexte de communication exolingue entre locuteurs non natifs. Plus particulièrement, nous nous intéressons à l’inférence comme stratégie en interaction. Nous proposons une étude empirique des inférences produites par des apprenants qui devaient exécuter une tâche de production écrite à partir de l’observation d’une discussion de leurs pairs. Nous exploitons des données provenant du dispositif groupe de discussion et suivi des pairs (Lebel et Viswanathan, 2016). Ce dispositif suscite des interactions orales authentiques et repose sur le concept de lacune d’information inhérent à la tâche communicative (Ellis, 2009). Il s’agit donc ici de considérer l’inférence en relation avec la tâche définie à partir de la lacune d’information. Cette étude a pour objectif d’identifier des pistes d’intervention didactique pour l’enseignement de la langue.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".