Metadiscourse in Research Writing: A Study of Native English and Pakistani Research Articles
Bibliographic record
Abstract
Metadiscourse is extremely important for structuring a relationship between writer and reader when it comes to academic writing. It is an interesting area of inquiry that is believed to play a vital role in writing persuasive discourse, based on the expectations of the people involved (Behzad & Shafique, 2018). This study deals with the comparative analysis of native English and Pakistani research articles. For this research, 100 native English and Pakistani English research articles are taken, following Hyland and Tse (2004a) model of metadiscourse. A corpus-based mixed method research approach is employed to carry out this study. All the metadiscursive devices are quantified by using corpus-based approach and then analyzed qualitatively. The results reveal that Pakistani research writers use more interactive markers whereas the interactional markers are found frequent in native English academic writers. The overall results disclose that Native research writers of English are more persuasive in their research writing as they guide the readers through text as well as involve them through different markers effectively.
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 | 0.006 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".