An Analysis of Lexico-Semantic Variations in Pakistani English Newspaper Corpus
Bibliographic record
Abstract
The present study focuses on the corpus-based lexico-semantic analysis of Pakistani English to identify the variations in the language of newspapers. It also investigates how Pakistani English newspapers consider their readers' cultural and social ideals for intelligible contact while selecting language posts. As a result, they often deviate from the native norms of the language adopting many indigenous linguistic features and emerging new varieties of New English to define their tasks easier in order not only to facilitate but to attract people’s attention. Therefore, Moag’s model on New Englishes, Boas’s theory of cultural relativism, and Kachru’s Theory of Nativisation and Acculturation with the conception of the Outer Circle (1986) mainly connected to institutionalize Second Varieties of English have been used for the theoretical analysis within Pakistani context dependent socio-cultural scenario. The usage of these lexical items shows that Pakistani English is derived from the source, namely, Standard British English, for example, shadi hall, Jihadi outfits, etc. These lexical item categories followed in coinage, borrowing, idiomatic collocations, and semantic shift. This study also attempts to create lexicographical entries to represent the diverse Pakistani English and become available to society's educational and global communication. The study purposively compiled the 2 million corpora from the websites of two major Pakistani English Newspapers, The Nation and The News, and then analyzed it by using corpus software tools Antconc 3.5.8w to search for the key terms and to identify these elements of the stance. The study's finding highlights the New English variety of Pakistani English Newspapers and the adapted lexemes used in the local sociolinguistics context. One of the study's most significant findings shows that the New Englishes lexemes are infused with Islamic, historical, and social culture, highlighting the diverse local colours adapted to the Pakistani setting. The New Englishes in Pakistan comprises the amalgamation of Arabic, Urdu, Punjabi, and English lexemes. The study also broadens the horizon of society's educational and communicational usage while maintaining the endonormative standard.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| 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) | 0.004 | 0.001 |
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".