Exploring the Process and Strategies of Chinese–English Abstract Writing Using Machine Translation Tools
Bibliographic record
Abstract
The present study explores English as a foreign language (EFL) learners’ processes and strategies when using machine translation (MT) tools in academic abstract writing. Eight EFL graduate students were introduced to translation-friendly writing strategies using Google Translate and were required to produce an English abstract with the aid of a machine translation tool. The study used qualitative and quantitative approaches in data collection and analysis. A triangulation process was developed and implemented, including think-aloud protocols during the writing session, surveys, and individual interviews after the writing session. The findings suggested that the translation-friendly writing strategies introduced to the participants were useful in enhancing the quality of their writing. Each participant demonstrated individual strategic uses of MT. Among the various strategies reported, back translation was the most commonly adopted one; that is, they first composed an abstract in Chinese (L1) and engaged in multiple rounds of translation between Chinese and English using MT; when problems were identified in the English abstract, they modified the Chinese abstract using translation-friendly writing strategies to enhance the quality of MT translation output. Most of the translation problems identified by the participants were related to non-academic expressions. While participants were satisfied with the quality of the abstracts produced with the aid of MT, they raised ethical concerns regarding the use of MT in academic writing. These findings suggest that MT has fundamentally changed the process of academic writing in English and call for the re-examination of the purpose of academic writing instruction and the approaches employed.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.025 | 0.183 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".