Exploring Interactive Metadiscourse of Chinese Science Postgraduates’ Academic English Writing
Bibliographic record
Abstract
The present study aims to explore the employment of interactive metadiscourse in Chinese science postgraduates’ academic thesis writing. Based on the interpersonal model of metadiscourse, this study analyzed the form, frequency and distribution of interactive metadiscourse, using thirty English abstracts of chemistry master’s theses selected from China National Knowledge Infrastructure during the period of 2021-2023 as corpus. The results showed that among all interactive metadiscourse, transition markers appeared most frequently and were distributed widely; endophoric markers and evidentials occurred less frequently and were distributed sparsely; the transition marker “and” and the code gloss “( )” were overused and frame markers were underused. Therefore, academic writing instructors should pay attention to cultivating students’ rhetorical awareness of using interactive metadiscourse in academic thesis writing to improve the organization of the abstract and make it more scientific. Through the above analysis, this research provides useful insights for academic writing in hard science, especially for improving the English academic writing skills of Chinese novice writers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".