Comparative Rhetorical Organization of ELT Thesis Introductions Composed by Thai and American Students
Bibliographic record
Abstract
Genre analysis is today’s dominant approach for textual analysis, especially in the ESP learning and teaching profession. Adopting this approach, the present study compares the Introduction chapters of MA theses in ELT (English Language Teaching) written by Thai students to those written by American university students based on the move-step analysis. Two sets of corpora comprise 30 TSI (Thai student Introduction) and 30 ASI (American student Introduction) Introduction chapters from the theses that followed the traditional five-chapter pattern or ILrMRD. All the TSI and ASI datasets were purposively collected from two electronic databases of graduate theses and dissertations, publicly known ThaiLis Digital Collection and ProQuest Dissertations and Theses. These were subsequently analyzed using genre analysis approach. The modified CARS model introduced by Bunton (2002) guides the move-step analysis. To ensure the coding reliability and consistency, the coding analysis of a subset of the entire datasets between the researcher and an expert coder was checked, and the coding agreement was at a highly satisfactory level. The findings demonstrated that both Thai and American MA students followed the moves and steps proposed in the framework to construct their Introduction chapters rhetorically. Both similarities and differences were discovered in the Introduction chapters investigated, in terms of the communicative purpose, the frequency of move-step occurrences, and the move-step classification. Pedagogical implications drawn from the present study are useful for EAP practitioners and research writing instructors, allowing ESL/EFL teachers to equip their graduate students with an appropriate rhetorical outline for thesis Introduction composition.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".