A Survey on College English Writing in China: A Cultural Perspective
Bibliographic record
Abstract
This survey investigates to what degree the Chinese learners know about the discrepancies between Chinese and English thought patterns and their possible effects on English writing. Eighty-one students from North China Electric Power University participate in the survey. Qualitative and quantities approaches, involving the adoption of both questionnaire and data analysis, underpin the survey. Questionnaire is used to examine five constructs, namely, students’ writing condition, students’ knowledge about discrepancies of the Chinese and English thought patterns and their effect on Chinese learners’ English writing in terms of wording, sentence structure and discourse organization. Data collected from the questionnaires are then analyzed. Findings from the study reveal that a vast majority of Chinese students are surprisingly not fully aware of the important influence thought patterns exert on languages and how discrepancies in thought patterns are associated with differences in languages. As a consequence of this, their English essays preserve some features of Chinese despite years of English learning. The results of the study contribute to a good understanding of the Chinese learners’ current writing condition in EFL teaching. Suggestions to alter this undesirable situation are put forward.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".