An Analysis of Research in Academic Prose between Native Speakers and Chinese Learners
Bibliographic record
Abstract
This study is a corpus-based lexical study that aims to compare the use of research as a noun between native speakers and Chinese EAP learners in research articles in Linguistics. A self-built learner corpus of academic English (CMFD) and its parallel corpus (PQDT) are applied. Quantitative analysis of frequency and qualitative analysis of collocation of node words are used in this paper. The results reveal Chinese EAP learners use research more frequently than native speakers, and native speakers never use “researches” as a plural form of noun in academic writing while Chinese EAP learners use this form frequently. Compared with native speakers, Chinese learners tend to make the following errors: an overuse of research; using research as a countable noun; disorder in using of “research” and “researches”; confusedness of “numerous research” expressions; mixed collocation prosodies. The knowledge gained by this study can increase awareness of proper use of research in composition of instructors and L2 writers, leading to clearer, more accurate texts.
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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".