Move Analysis in the Introduction Chapter of Thesis Written by Native English Speakers and Non-Native English Speakers
Bibliographic record
Abstract
Genre analysis is essential for understanding rhetorical strategies and enhancing communication for academic purposes. However, constructing effective introduction chapter of PhD (ICPhD) thesis remains challenging due to the diverse rhetorical moves influenced by cultural and linguistic backgrounds. This study employs a corpus-based approach to quantitatively and qualitatively analyze the rhetorical moves in 40 ICPhD theses from top universities in Australia and Malaysia submitted between 2017 to 2022 using Bunton’s (2002) rhetorical move model. The results reveal significant differences between the two corpora in terms of move frequency, sequence, and cyclicity, as well as the emergence of a new rhetorical move shared by both corpora, especially in Malaysian ICPhD theses. Additionally, the study compares these findings with past research on Australian ICPhD theses (Pawase, 2018), adopting both synchronic and diachronic perspectives to explore the unique challenges posed by language, culture, and educational norms to PhD thesis writers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".