Investigating Grammatical Colloquial Features in EFL Learners’ Theses by Chinese English Learners
Bibliographic record
Abstract
<p>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.</p>
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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 | 0.170 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".