Contrast of Schema Model and Discourse Analysis Model in the Teaching of English Reading
Bibliographic record
Abstract
The traditional teaching practice of English reading in China put emphasis only on word-by-word explanation, syntax analysis and translation exercises. Nowadays, the two theories, the schema theory and the discourse analysis, have been introduced to our teaching practice of English reading. First, this paper gives a brief introduction to the two theories and their applications in the teaching practice of English reading. Then, it makes a contrast between the two theories and between their applications in the teaching practice of English reading. In conclusion, it points out the contribution of both theories made to the teaching practice of English reading and the advantages of discourse analysis over schema theory. Key words: schema theory, discourse analysis, the teaching practice of English reading Resume: Par rapport a la pratique traditionnel qui n’attache de l’importance qu’a la l’interpretation litterale, l’analyse syntaxique et l’exercice de traduction, deux nouvelles theories sont introduites aujourd’hui dans l’enseignement de la lecture en anglais de notre pays, a savoir la theorie graphique et la theorie de l’analyse du discours. L’article presente d’abord ces deux theories et leur application a l’enseignement de l’anglais, compare et analyse ensuite les differences des deux modeles et enfin evalue sommairement les avantages et desavantages de leur application a l’enseignement de l’anglais. Mots-cles: theorie graphique, analyse du discours, enseignement de la lecture en anglais 摘要：相對于傳統英語閱讀教學只重視逐字釋義、句法分析和翻譯練習的做法，如今我國英語閱讀教學新引進了兩個理論，即圖式理論和語篇分析理論。本文首先分別介紹了圖式理論和語篇分析理論及其在英語教學中的運用，然後從各方面對比分析了兩個模式的不同，最後簡要地評價了兩者在英語教學運用中的優劣。 關鍵詞：圖式理論；語篇分析；英語閱讀教學
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".