An Investigation of Abstract and Discussion Sections in Master’s Dissertations
Bibliographic record
Abstract
This study analyzes the differences between the academic writing of undergraduate students belonging to two Pakistani universities, one located in an urban setting and the other in a rural locale, in an attempt not only to identify why these differences may arise but also how such learners may be encouraged to more readily adopt academic writing techniques in their theses. Data comprises the abstract and discussion sections of undergraduate students’ dissertations. The study uses Swales’ CARS model to analyze the academic writing proficiency demonstrated in the selected data. The study finds that the occurrences of a particular move were more frequent in the dissertations of the rural area students. In contrast, the instantiation of hedges was significant in the dissertations of learners from the urban area university. These observed differences confirm the perception that in terms of academic writing “quality”, the universities in rural settings in Pakistan are not sufficiently competitive with peer institutions in urban settings. The study further reveals that dissertations from rural setting universities reflect poor use of rhetorical moves associated with good academic writing, while in line with Swales’ CARS model, students from the urban university show significant linear patterns and accuracy in their academic writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".