Grammatical Cohesion in the Introduction Chapters of Linguistics Ph.D. Theses Written by Anglophone Academic Writers
Bibliographic record
Abstract
Effective formal writing skills are crucial for educational success and improved academic status; however, writing in English can be challenging, especially for inexperienced English writers and for those individuals for whom English is not their primary language. One often experienced challenge is the appropriate use of cohesive devices (CDs) to organize texts and enhance their semantic potential. Thus, this study analyzed the grammatical CDs utilized by Anglophone Academic Writers (AAWs) in the introduction chapters of 45 Ph.D. theses (86,000 words) in the field of linguistics using Halliday and Hasan’s CDs taxonomy. The analysis showed that AAWs mainly employed references, particularly personal and demonstrative, followed by conjunctions, particularly additive ones, as their dominant CDs. Additive conjunctions helped establish the coherent progressions of ideas in the introductions, while personal references, especially first-person pronouns, conveyed authorial presence. Demonstrative references emphasized important ideas discussed in the theses. The strategic application of these grammatical CDs effectively directed readers through the introductions, creating a logical flow for the writers’ points. Therefore, this study presents valuable insights into the cohesive writing practices of AAWs, which can be used to enhance writing instruction and help novice English writers in improving their formal writing skills.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".