Cultural Factors in EAP Teaching — Influences of Thought Pattern on English Academic Writing
Bibliographic record
Abstract
In the last decade, more and more EFL teachers in the universities of China have been aware of the feasibility and necessity of teaching English for Academic Purpose (EAP), which is identified as one type of English for Specific Purposes, to students of non-English majors. Among the EAP courses, academic writing is considered as the most helpful one. More and more scholars of ESP in China have conducted researches on English academic writing (EAW) including analysis on the syntactic characteristics of English for academic purposes, corpus-based study of English dimension adjectives in academic speaking and writing, and comparative study on Natives’ EAW and Chinese EAW. It was pointed that the EAW research in China focuses on language form and rules, but neglects the correlation of contents and thoughts. Therefore, this research studies the influences of cultural thought patterns on English academic writing by employing product approach to contrast vocabulary and discourse differences in EAW writings produced by Chinese students and native English students. Key Words : English for Academic Purpose (EAP); Cultural factors; Thought pattern; English Academic Writing
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".