A Corpus-based Study of Modal Verbs in Chinese Learners’ Academic Writing
Bibliographic record
Abstract
While more Chinese students are going abroad to persue their further academic study, how to help them improve academic writing competence has received wide attention. Modality, as one of the complex areas of English grammar, reflects the writer’s attitude and is extremely important in academic written discourse. Therefore, it is necessary to investigate how Chinese learners of English use modal verbs. For this purpose, a learner corpus (LC) with Chinese learners’ academic writing has been compiled and compared against a professional corpus (PC) which consists of published research articles. With the help of software Antconc 3.2.4w, the use of nine core modal verbs in both corpora has been explored. Findings indicate that compared with professional writers, Chinese learners tend to use modal verbs more frequently; they also tend to overuse can, will, could and would and underuse may. Based on an analysis of the two corpora, this study proposes possible reasons that account for these differences. This study provides some insights into the use of modal verbs by Chinese learners of English and thus informs teaching of modal verbs in the English classroom and contributes to the academic writing curricula design.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 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".