Noun Phrase Complexity in Academic Writing: A Comparison of Research Proposals Written by Chinese EFL and Malaysian ESL Postgraduates
Bibliographic record
Abstract
An increasing body of scholars have investigated noun phrase complexity in L2 English writing from varied perspectives, but few of them focus on the differences of the English writing produced by EFLs and ESLs. Thus, the study explored how three international postgraduates from China and three local Malaysian postgraduates in a top university of Malaysia differ in noun modification. The noun modifiers in their research proposals were coded, categorized, counted, and compared. Based on the findings, the EFLs used premodifiers more frequently than the other group, especially for attributive adjectives and nouns as premodifiers, while the ESLs made more frequent use of advanced postmodifiers, including prepositions other than ‘of’ as postmodifiers to express both concrete/locative meanings and abstract meanings, and multiple prepositional phrases with levels of embedding. The findings highlighted the need to implement explicit individualized instruction for the students with different L1 backgrounds but within the same classroom.   
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".