Research on Writing Samples from the Perspective of Metadiscourse
Bibliographic record
Abstract
Writing, as an advanced model of output, not only conveys the subject but also realizes the communication between readers and writers. Metadiscourse can help writers arrange and organize the discourse to influence readers’ understanding of the text and their attitude towards its content. Taking writing samples of College English Test Band 4 (CET-4) as corpus, the research aims to explore the use of metadiscoure markers in high score writing group (HG) and low score writing group (LG). The research questions to be addressed in the study are as follows: 1) What are the similarities and differences between the two groups in the quantity and the types of metadiscourse markers? 2) What are the similarities and differences between the two groups in choosing metadiscourse markers? 3) What’s the overall distribution of the inappropriately used metadiscourse markers in two groups? The research results show that there is a positive relation between proper use of metadiscourse markers and writing quality. This paper puts forward the strategies of improving the students' ability in the proper use of metadiscourse markers in English writing teaching.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".