A Comparative Analysis of the Generic Structure of RA English Abstracts in Chinese-Medium and English-Medium Linguistics Journals
Bibliographic record
Abstract
<p>This paper aims to analyze the generic structure of English abstracts in both Chinese-medium and English-medium linguistics journals. A total of 40 abstracts published in the year of 2011-2013 are collected randomly, with 20 written by native English speakers from <em>Applied Linguistics </em>and <em>Language</em> and the other 20 by Chinese scholars from <em>Journal of</em> <em>Foreign Languages</em> and <em>Foreign Language Teaching and Research</em>. The BIMRD/C model is adopted in this study as distinct differences can be found in the two corpora in terms of the Background move. Three major differences are revealed. Firstly, the abstracts written by native English speakers are more complete in structure than those by Chinese writers as they tend to omit the Background move and the Discussion/Conclusion move. Secondly, most Chinese writers prefer to combine the Method move with the Introduction move and put it at the very beginning of the abstract, while native writers tend to use the independent and the integrated Method nearly equally. Thirdly, in the Results move, Chinese scholars tend to objectively report their study results in detail by <em>“Results indicate that…”</em>, sometimes listing them, while native English writers sometimes choose to highlight their research results by patterns of <em>“we find (show, propose) that” </em>and<em> “I propose”</em> although most of them also use such objective patterns to present their research results. This study is especially helpful for those Chinese writers who hope to publish their paper in international journals.</p>
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 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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".