A Comparative Study on the Lexical Collocations in Academic Discourse by International Scholars and Chinese EFL Learners
Bibliographic record
Abstract
Lexical collocations are essential in academic English and present considerable challenges for L2 learners. This study compiled three text corpora: one from academic written texts published in top international linguistics journals, and the others from MA theses and doctoral dissertations of Chinese college students majoring in Foreign Linguistics and Applied Linguistics. An Academic Collocation List (ACL) was initially created from the journal articles and then the collocation lists of the top 200 verbs were compared across the three corpora. The study identified potential collocates for over 2,400 content words in the ICAE (International Corpus of Academic English) journal articles, resulting in 375 potential combinations. Among these, noun combinations (adj+n, n+n) accounted for nearly 70% of the total entries, followed by verb+noun/adj combinations, adv+adj combinations, and verb+adv combinations. Statistical analysis revealed significant differences between Chinese EFL learners and international scholars, with a larger discrepancy observed between MA theses and journal articles than between doctoral dissertations and journal articles. Chinese EFL learners tended to overuse a limited set of collocations that, while grammatically correct, often sounded unnatural to native speakers. Additionally, the verb+noun combinations used by Chinese EFL learners were frequently physically and semantically different from those employed by international scholars. These findings underscore the need for targeted pedagogical interventions to improve the collocational competence of Chinese EFL learners.
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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 | 0.014 |
| 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".