Lexico-Semantic Features of Pakistani English Newspapers: A Corpus-Based Approach
Bibliographic record
Abstract
Lexico-semantic variation with its socio-cultural significance, genuinely instilled in the tint of adaptation as well as innovation, has prompted many researchers and linguists to explore profoundly within the prolific soil of Pakistani English, while formulating its mark within the sphere of “New Englishes”. This “norm-developing” variety traces the accelerating concern in the momentum of diffusion of “World Englishes”, and consequently, adding to the evolution and development of English in non-native-contexts with its deep enriching essence. The principal goal of the present research work is to investigate lexico-semantic variation in Pakistani English newspapers along with the functional outcomes within the vibrant mould of perceptions based on context-dependent Pakistani socio-cultural scenarios in the light of Moag’s model based on “New Englishes”, Boas’ Cultural Relativism, Kachru’s Nativization and Acculturation with the conception of “Outer Circle” mainly connected to institutionalized Second Varieties of English. The study further analyzes two major lexico-semantic categories based on the corpus derived from two leading Pakistani English Newspapers. This corpus-based study took out the sample texts with their usual lengths, purposively chosen, from the real life. Findings of the study highlight that lexico-semantic categories in Pakistani English Newspapers are infused within the local aroma while having a diversity of colours of usage and elucidations in multifaceted functional settings of Pakistan. The study broadens the horizons while opening a wide range of opportunities for the researchers in grammar, lexicon, semantics, pragmatics etc. by means of construction and extension of corpora and drawing comparison with the other varieties of English.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".