The Use of Metadiscourse by Saudi and British Authors: A Focus on Applied Linguistics Discipline
Bibliographic record
Abstract
This study investigated the use of metadiscourse tools by Saudi and British authors in Applied Linguistics discipline. In particular, the study tried to identify the kinds of metadiscourse markers used by Saudi and English authors in ALRAs and to determine the most and least frequent metadiscourse makers. In order to achieve these goals, (10) ALRAs written by Saudi authors and (10) ALRAs written by British authors served as the corpus of the study. The research articles were selected from well-known journals and published between 2010 – 2018. Hyland's (2005) model was used to find out the distribution of metadiscourse markers in each type of corpora. The findings showed interactive metadiscourse markers are used more than the interactional metadiscourse markers. Compared with the British authors, the Saudi authors were found to use metadiscourse markers more than the British authors. The Saudi authors employed all metadiscourse sub-categories more frequently than the British authors except frame markers, evidentials, endophoric markers, and self-mentions. In addition, it was found that transitions were the highly frequent metadiscourse markers in the whole corpora, followed by hedges, evidentials, boosters, and attitude markers, respectively. On the other hand, engagement markers were the least frequent metadiscourse markers in the whole corpora. 
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".