A Study on the Use of Lexical Chunks by Chinese EFL Learners in Writing
Bibliographic record
Abstract
<p>Lexical Approach put forward by Michael Lewis (1993) is widely acknowledged in EFL teaching and lexical teaching is very important development in the evolution of language teaching (Lowe, 2003).<strong> </strong>For about thirty years of teaching English as a foreign language (EFL) in China, more and more teachers have realized the importance of teaching and encouraging learners to use ready-made lexical chunks. However, the present study focuses on the overuse of lexical chunks in learners’ writings in a high stake national test (College English Test Band 6 – CET6). The corpus-based data analysis will be done to find the most commonly used lexical chunks by Chinese EFL learners and demonstrate what is meant to be the overuse of lexical chunks. Furthermore, the reasons for misuse and overuse of lexical chunks will be discussed. The findings drawn from structural and functional analysis of lexical chunks also have some pedagogical implications.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".