A Study of Pakistani English Newspaper Texts: An Application of Halliday and Hasan’s Model of Cohesion: A Discourse Analysis
Bibliographic record
Abstract
This article aims to examine the patterns of each type of cohesive device in light of the cohesion model proposed by Halliday and Hasan in 1976. Halliday and Hasan identified five different types of cohesion: reference, substitution, ellipsis, conjunction and lexical cohesion in the text. This study uses the selected weekly articles authored by Cyril Almeida from well-known daily published English Newspaper “The Daily Dawn”. Analysis of text comprises Halliday and Hasan’s cohesion model, and analyzes linguistic techniques used in newspaper texts. The study finds repeated occurrences of cohesive devices such as referencing, substitution, ellipsis, conjunction, and lexical cohesion. Moreover, reiteration is found to be the most frequently occurring cohesive device. Reference from grammatical cohesion also outnumbers all other subcategories of cohesion. In addition, many of the literary terms employed in articles make it diverse in uncovering some of the political contexts to the audience. Hence, it concludes that in the overall occurrences of lexical cohesion, reiteration and collocation are dominant; suggesting that the texts of selected news articles of Cyril Almeida are cohesive mainly because of lexical cohesion, i.e. semantic linkage through vocabulary rather than grammar.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".