Exploring Lexico-grammatical Patterns in Doctoral Dissertations: A Multidimensional Analysis
Bibliographic record
Abstract
Doctoral dissertations occupy a prominent place in academic communication and advancement. This genre applies its intricate lexical and grammatical patterns to shape its effectiveness and these patterns need to be studied. The study aims to investigate the discussion section of the doctoral dissertations from ten leading British universities. The objective of the study is to explore the lexico-grammatical similarities and differences between ELT and Linguistics dissertations. It also attempts to analyze how far the lexico-grammatical choices used in the discussion section of ELT dissertations are similar to or different from those of Linguistics. The corpus used in this study consists of 200 discussion sections: 100 from ELT and 100 from Linguistics. A random sampling technique was used to collect data which consisted of publicly available dissertations. Biber’s Tagger was used to annotate grammatical and lexical features. To find out the significant interaction between the selected disciplines, a factorial ANOVA was used. The study is significant in terms of providing insight into the preferred lexico-grammatical patterns used by the students. It further explains how two different disciplines – Linguistics and ELT, make various lexico-grammatical choices. The findings of the study will help the researcher from around the world, particularly the ones from non-English speaking countries, in terms of familiarizing them with the conventions involved in writing a dissertation.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".