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.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".