English Oral and Maxillofacial Surgery Research Articles: Move and Phrase Frames in the Introduction Sections
Bibliographic record
Abstract
While many previously published studies have investigated the moves, steps and their structure of research article (RA) sections in various academic fields, such studies are rare in the discipline of Dentistry. Besides, prior research has been conducted on phrase frames of RAs in some fields, but phrase frames in Dentistry RAs have received scant attention. Hence, the current study aims to explore the rhetorical structure and phrase frames of English RA Introduction sections in the Oral and Maxillofacial Surgery (OMS) sub-discipline of Dentistry. A total of 30 articles from 3 renowned journals were collected and their Introductions were analyzed based on the frameworks of Kanoksilapatham (2005) for the move structure and Gray and Biber (2013) and Simpson-Vlach and Ellis (2010) for phrase frames, respectively. The results showed that the OMS Introductions exhibit 3 moves, i.e., Move 1: Announcing the importance of the study, Move 2: Preparing for the present study, and Move 3: Introducing the present study, and 3 new steps that illustrated the disciplinary features of Dentistry. The study also found 46 phrase frames in the structural and functional categories. These findings provide insights about the two significant rhetorical features of OMS RA Introduction sections and serve as useful resources in the English for Dentistry Purposes (EDP) teaching, learning, and research publication.
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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 | 0.016 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".