Revealing Disciplinary Variation in Pakistani Academic Writing: A Multidimensional Analysis
Bibliographic record
Abstract
Pakistani English as a non-native variety exhibits variation at different levels of language. Early quantitative studies on Pakistani English have compared individual linguistic features of Pakistani English with their counterparts in British English and claimed about the distinctive identity of Pakistani English as an indigenous variety. Pakistani English need to be compared at the level of register to further highlight its unique features and strengthen its distinct identity. Based on a special purpose corpus, the present research paper endeavors to investigate linguistic variation across disciplines in Pakistani academic writing as a register. Disciplinary variation is explored along with five new textual dimensions identified and labeled through the technique of Multidimensional analysis (Azher & Mehmood, 2016). The ANOVA results reveal that statistically significant differences are found among disciplines on all the new dimensions of Pakistani Academic Writing. The findings underline the implications for discipline-specific and register-based pedagogies with special reference to Pakistani English.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".