The Metadiscourse Markers in Good Undergraduate Writers’ Essays Corpus
Bibliographic record
Abstract
Good student writers are often seen as a benchmark to the weaker writers. In the area of metadiscourse in students’ writing, previous studies show that good undergraduate writers use more metadiscourse markers as compared to the weaker writers. However, there are limited previous studies which observe the use of metadiscourse among Malaysian ESL undergraduate writers whose Bahasa Malaysia is their first language. Therefore, further studies involving undergraduate writers from this particular setting is significant to add more literature to the field of metadiscourse among ESL undergraduates. This paper aims to present the metadiscourse markers found in a corpus of good undergraduate writers’ essays (GUWE corpus). These metadiscourse markers are classified in the main categories and sub-categories based on Hyland’s (2005) interpersonal model of metadiscourse. Using a concordance software, this study aims to reveal the frequency of the metadiscourse markers use in good essays produced by 269 Malaysian undergraduate writers. The findings presented in this paper are hoped to be useful for other researchers who are interested in the same field of metadiscourse among ESL student writers especially among Malaysian undegraduates.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".