A Comparative Genre-Based Analysis of Move-Step Structure of RAIs in Two Different Publication Contexts
Bibliographic record
Abstract
This genre-based study investigates the move-step structure of two sets of English-medium research article introductions (RAIs) in the field of applied linguistics using Swales’ (1990, 2004) Create a Research Space (CARS) model of move/step analysis. A corpus of 30 RAIs from two English-medium research articles (15 International and 15 Local) was selected. The international research articles written for an international readership were selected from the journal English for Specific Purposes, while the local research articles, written for local readers, were selected from Arab World English Journal. The findings indicated that although the three moves suggested by the CARS (Swales, 1990, 2004) model appeared in the two subcorpora, some variation was observed with respect to the range of moves employed in each subcorpus. As expected, Move 2 was not always found in texts in the Local subcorpus. In terms of steps and sub-steps analysis, the findings showed the three steps and sub-steps of Move 1 are conventional in the International and Local applied linguistics RAIs. Further, while M2-S1B is conventional and M2-S1A is optional in the Local subcorpus, these two sub-steps of Move 2 are conventional in the International subcorpus. There were no striking differences between the two subcorpora with regard to the employment of the proposed steps of Move 3. Limitations and the implications of the findings, as well as recommendation of some suggestions for future research are provided.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| 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".