A Contrastive Study on Lexical Bundles in Argumentative Writing by L1-Chinese and L1-English Undergraduates
Bibliographic record
Abstract
This study compares the use of lexical bundles in argumentative essays written by L1-Chinese students and L1-English students through a corpus-based approach. The data consist of two corpora: a L1-Chinese corpus with 506 English argumentative essays produced by Chinese undergraduates in disciplines related to science and engineering and a L1-English corpus with 207 argumentative essays written by L1-English undergraduates. The identified lexical bundles were analyzed both structurally and functionally. The findings suggest that L1-Chinese students used significantly more types and tokens of lexical bundle structures than L1-English students, and also employed all the three functional categories of bundles more frequently than L1-English students. In addition, L1-Chinese students’ writing was marked by a higher preference for clause-based bundles, which features the academic writing of lower-proficiency writers, and a wide use of conversational bundles (e.g. a lot of people), which implies their lack of awareness of academic register. The pedagogical implications are then provided regarding lexical bundles for ESL teachers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".