Linguistic Variation across Research Sections of Pakistan Academic Writing: A Multidimensional Analysis
Bibliographic record
Abstract
With the concept of language variation, it has become utmost important to analyze linguistic patterns across register. Pakistani academic writing like other registers in Pakistan is an area that still seeks the attention of the researchers and linguists. This target register needs to be fully described in terms of linguistic characteristics to strengthen the distinct identity of Pakistani academic writing as a register. The present research strives to explore linguistic variation across research sections of Pakistani academic writing as a register along with five new textual dimensions explored through the technique of Multidimensional analysis (Azher & Mehmood, 2016). The research is based on the corpus of 235 M. Phil and PhD research dissertations taken from different universities all over Pakistan. The corpus was further divided into five research sections and was tagged for 189 linguistic features. The ANOVA results on variation among research sections indicate that there lie statistically significant differences among research sections of Pakistani Academic Writing on all the new textual dimensions.
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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 | 0.002 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".