Investigating Move Structure of English Applied Linguistics Research Article Discussions Published in International and Thai Journals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the rhetorical move structure of English applied linguistic research article Discussions published in Thai and international journals. Two corpora comprising of 30 Thai Discussions and 30 international Discussions were analyzed using Yang & Allison’s (2003) move model. Based on the analysis, both similarities and differences regarding the move occurrence, move-ordering patterns, and move cyclicity were found. The marked differences of the two corpora were in the step employment. The findings obtained in the current study are useful particularly for novice non-native writers by facilitating them to better understand the rhetorical structure of research article Discussions in the different publication contexts. In addition, they may provide L2 teachers with insight into effective instructional strategies to help EFL/ESL learners acquire pragmatic knowledge of the rhetorical structure of research article Discussions.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it