Cross-Cultural Analysis of the Use of Hedges in European and Pakistani English Newspaper: A Corpus-Based Study
Bibliographic record
Abstract
Language, discourse and communication reveal social and cultural inclinations of human civilization (Van Dijk, 1997). Language behavior is exhibited through communication which is extracted from three main categories of language; “ideational, interpersonal and textual” (Halliday, 1978, 1985). Hedges are interpersonal metadiscourse markers (Hs), lexical devices that authors employ to arrange their discourse and communicate their standpoint about the substance for the reader. Cultural and linguistic background of the author may affect the employment of hedges in the discourse. The present study investigated the interpersonal metadiscourse marker-hedges- in the Culture section of European English Newspaper (CEEN) and Pakistani English Newspaper articles (CPEN) based on Hyland’s classification (2004). The quantitative corpora-based study contained 32 articles from culture section of Pakistani English newspaper: Dawn News (DN) and 32 articles from culture section of European English Newspaper: BBC. The articles from each newspaper were extracted from online resources. Two corpora have equal representation of words, 40000 each. Data analysis was done using SPSS 22 to see the frequency of hedges used in the data. Moreover, an independent sample t-test was applied. It was found that there is a meaningful difference between the European and Pakistani English newspapers’ usage of hedges. This research would help not only ELT practitioners to teach how hedges change the genre of discourse, but would also shed light on cultural discourse. It would depict how the same hedges are used in two different cultural discourses revealing distinct culture and identity.
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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 | 0.000 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".