Bibliographic record
Abstract
Teacher talk is usually viewed as one of the decisive factors of success or failure in classroom teaching. Based on the basic principles of Relevance Theory, the present thesis has focused on the teacher-student interaction in form of question-answer in language classrooms and tried to analyze teacher talk under the framework of Relevance Theory to prove that Relevance Theory is able to provide an explanation for teacher talk. This thesis proposes some pedagogical implications for successful teacher talk and teacher-student interaction in EFL context. Keywords: teacher talk; teacher-student interaction; Relevance Theory Resume: Le discours de l'enseignant est generalement considere comme l'un des facteurs decisifs de succes ou d'echec dans l'enseignement en classe. Basee sur des principes elementaires de theorie de la pertinence, la presente these a mis l'accent sur l'interaction enseignant-eleve sous forme de question-reponse dans les cours de langue et tente d'analyser les discours de l'enseignant dans le cadre de la theorie de la pertinence afin de prouver que la theorie de la pertinence est capable de fournir une explication pour le discours de l'enseignant. Cette these propose quelques implications pedagogiques pour les discours de l'enseignant et de l'interaction enseignant-eleve reussis dans le contexte de EFL.Mots-cles: discours de l'enseignant; interaction enseignant-eleve; theorie de la pertinence
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".