Reporting Verbs in ELT Research Discussions: A Corpus-Based Comparison of Thai Scholars and International Editor-Authors
Bibliographic record
Abstract
Academic writing often requires authors to position their work in relation to previous studies, frequently through the use of reporting verbs (RVs). These verbs are essential rhetorical tools for presenting findings, evaluating prior research, and engaging with readers. This study investigates the use of RVs in the discussion sections of English language teaching (ELT) research articles written by Thai scholars (TS) and international editor-authors (IE), the scholars who also hold editorial roles in top-tier journals. Drawing on Hyland’s (2002) classification of RVs, the study compares the frequency and rhetorical functions of the most commonly used RVs in two balanced corpora of 20 discussion sections each. Using both corpus-based and qualitative content analysis, the study reveals key cross-cultural differences. Thai scholars tended to employ more RVs overall and showed a stronger preference for discourse-oriented verbs, while international editor-authors favored research-oriented verbs. These findings highlight differing rhetorical conventions in academic writing and suggest that a deeper awareness of RV use may help novice scholars enhance the clarity and persuasiveness of their academic writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".