The Syntactic Variations in Verb Phrase – Double Modals in Pakistani English (PakE) and Malaysian English (MyE): A Comparative and Corpus-Based Study
Bibliographic record
Abstract
This study aims to detect the variations and compare the use of Verb Phrase – Double Modals in PakE and MyE. For this study, two recently released corpora – GlowbE-PK and GlowbE-MY were utilized. Twenty Verb phrases with double Modals in PakE and MyE were selected from thousands of concordance lines and a large amount of text. This study utilized mixed method research and also kept in view the Sociolinguistic Variation and World Englishes conventions of research by utilizing corpora.The Frequency of each token (phrase), and then the accumulative and average frequencies of both Englishes were given separately. A comparative analysis of both varieties of English was done. It was an interesting fact to know that MyE displays comparatively more use of VP-Double Modals than PakE. The frequency of VP - Double Modals in MyE is 1.88, which is 0.41 more than the frequency of VP – Double Modals in PakE.The results and discussion indicated a noticeable variation in the use of VP with Double Modals of PakE and MyE from Standard British English (BrE). This research also indicates a linguistic pattern in the different dialects of English. The PakE and MyE have different phases of development, and contact languages with English in both varieties are also different. However, they both display the use of Double Modals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".