Thematic Structure and Thematic Progression in Reading Texts in Vietnamese High School English Textbooks
Bibliographic record
Abstract
Thematic choices and thematic development are crucial for constructing a meaningful and coherent text, facilitating readers' comprehension. Although extensive studies on textbook analysis have explored either thematic structure or thematic progression patterns, these two aspects seem to have been collectively under-investigated. This study attempts to investigate the differences in thematic organizations that characterize 20 reading texts in English 10 and English 12 textbooks for high school students in Vietnam, to investigate how thematic features in different textbook levels aligns with learners' proficiency levels. Two analytical models, including Halliday's (1994) theme categorization and McCabe's (1999) thematic progression patterns, were adopted. The findings reveal a predominant use of simple, mainly unmarked topical themes in both books, with an increased presence of multiple and clausal themes in English 12. The results also indicate a dominant use of constant, simple linear, and miscellaneous progression patterns, while more complex structures such as derived hyper-themes and split rhemes are rare, and split themes are entirely absent. Hence, these research results suggest a strategic approach to textbook design aimed at enhancing linguistic proficiency and reading comprehension through increased thematic complexity across different language levels, which can offer valuable pedagogical implications and inform future research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".