Investigating Grammatical Colloquial Features in EFL Learners’ Theses by Chinese English Learners
Bibliographic record
Abstract
Researches into colloquialisation in academic writing have become increasingly popular in recent years. However, little has been conducted to the dimension of grammar. Thus, through the corpus-based quantitative and qualitative analysis method, the present study compiled three corpora extracted from Chinese MA theses, PhD dissertations and international journals, aiming to explore the grammatical colloquial features and non-colloquial features in Chinese EFL learners’ theses. Compared with international journals, both MA theses and PhD dissertations displayed strong colloquial tendency. The similarities between MA theses and PhD dissertations outweigh their differences. Besides, doctoral dissertations are not less colloquial than MA theses. The statistical evidence suggests that the EFL learners in China lack the register consciousness of academic writing and fail to comply with the conventional pragmatic paradigm of academic discourse. With the intention to deepen EFL learners’ stylistic awareness and decrease their colloquial tendency, the study offers some suggestions, seeking for the pedagogical implications for English academic writing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".