Application of Coherence of Theme and Rheme System in English Language Teaching under Cognition
Bibliographic record
Abstract
The repetitive coherence of theme and rheme comes through one of the important cohesive devices which make texture coherent and progressing. From cognitive point of view, applying the mode of thematic and rheme into English language teaching can display its tool function for texture. It also contributes to the understanding and interpretation of coherent discourses, and to the way in teaching reading and writing. Therefore， learners will leap forward from their cognitive level. Key words: cognition, coherence of theme and rheme, English language teaching Resume: La cohesion reiterative theme/rheme dans le texte est un moyen important pour realiser la cohesion et la coherence du texte. C’est justement ces moyens de cohesion qui permettent de realiser la progression theme/rheme et de composer ainsi un texte coherent. L’application du mecanisme cohesif de theme/rheme dans l’enseignement textuel de l’anglais permet de montrer sa fonction comme outil textuel, de fournir un fil de pensee clair pour la redaction et la lecture en anglais et d’approfondir ainsi la cognition des apprenant sur l’anglais. Mots-cles: cognition, cohesion theme/rheme, enseignement textuel 摘要：語篇中主述位的反複銜接是實現語篇銜接和連貫的重要手段之一 ,而正是這些語篇銜接的手段使得主、述位達到層層推進，構成連貫的語篇。從認知的角度將主述位銜接機制應用於英語篇章教學，可展示其作為篇章工具的使用性，也可以為英語寫作和閱讀教學提供更為清晰的思路，使英語學習者從認知上達到一個飛躍。 關鍵詞：認知；主述位銜接；篇章教學
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".