Analyses of Move Structure and Verb Tense of Research Article Abstracts in Applied Linguistics
Bibliographic record
Abstract
This study examined 90 research article abstracts in three applied linguistics journals (i.e., TESOL Quarterly, Applied Linguistics, and Language Learning) from two dimensions: the move structure features and the verb tense of each move. The results showed that the abstracts analyzed tended to take a four-move structure instead of a five-move one as proposed in literature. In addition, since some publishers have word limits on abstract length, authors would usually follow the publisher’s guideline accordingly, thus there existing some differences concerning the move structure features among the abstracts in the three journals. In terms of the verb tense in each move, the preferred pattern was as follows: the present tense usually occurred in the first, second, and fifth move, while the past tense was often used in the third and fourth moves. It was also found that there were some variations between the abstracts written by native speakers and nonnative speakers of English. It is hoped that with detailed analyses of abstracts, the results of this study may serve as a complement to the guidelines for novice writers to construct a proper research article abstract in applied linguistics.
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 | 0.011 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".