Metadiscourse Analysis of Pakistani English Newspaper Editorials: A Corpus-Based Study
Bibliographic record
Abstract
Metadiscourse markers (MMs) are lexical resources that writers use to organize their discourse and express their stance about the content or the reader. Metadiscourse analysis of Pakistani English Newspaper Editorials (PENE) has been conducted. The corpus of this study has contained 1000 editorials taken from four renowned Pakistani newspapers: Dawn News (DN), The Frontier (TF), The Express Tribune (TET) and The News (TN). The distribution of 250 editorials from each newspaper has been retrieved from online sources. The frequencies of metadiscourse features (MFs) have been counted and compared, and further studied metadiscourse features (MFs) functionally on the basis of propositional and non-propositional contents. A comprehensive model on Interpersonal metadiscourse has been proposed and it has been categorized into interactive and interactional markers. A comprehensive scheme of metadiscourse markers (MMs) has been proposed for the analysis of the present study. The findings revealed that all corpora used more interactive than interactional markers. In this regard, the sub-categories of interactive metadiscourse such as sequencing markers and transition markers have been frequently observed in the corpus of The Frontier (TF) as compared to other said corpora. The sub-categories of interactional metadiscourse such as engagement, and hedges have been frequently seen in the corpus of The Frontier (TF) as compared to other said corpora. In conclusion, this study has claimed that The Frontier (TF) is more reader-friendly because of the excessive use of interactive metadiscourse.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".