Intensifier-Verb Collocations in Academic English by Chinese Learners Compared to Native-Speaker Students
Bibliographic record
Abstract
Abstract It is difficult for L2 English learners in general, and especially Chinese learners of English, to form idiomatic collocations. This article presents a comparison of the use of intensifier-verb collocations in English by native speaker students and Chinese ESL learners, paying particular attention to verbs which collocate with intensifiers. The data consisted of written production from three corpora: two of these are native English corpora: the British Academic Written English (BAWE) Corpus and Michigan Corpus of Upper-Level Student Papers (MICUSP). The third one is a recently created Chinese Learner English corpus, Ten-thousand English Compositions of Chinese Learners (TECCL). Findings suggest that Chinese learners of English produce significantly more intensifier-verb collocations than native speaker students, but that their English attests a smaller variety of intensifier-verb collocations compared with the native speakers. Moreover, Chinese learners of English use the intensifier-verb collocation types just-verb, only-verb and really-verb very frequently compared with native speaker students. As regards verb collocates, the intensifiers hardly, clearly, well, strongly and deeply collocate with semantically different verbs in native and Chinese learner English. Compared with the patterns in Chinese learner English, the intensifiers in native speaker English collocate with a more stable and restricted set of verb collocates.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".