A Corpus-Based Comparative Study on Syntactic Complexity in University Students’ EFL Writing in Southwestern China: A Case of Pu’er University
Bibliographic record
Abstract
Syntactic complexity is the variety and sophistication degree of the syntactic structures conveyed in written production. The syntactic complexity of general Chinese university students’ EFL writing has been studied previously, but the performance of university students in educationally underdeveloped Southwestern China remains unclear. Taking Pu’er University as a case, this study collected 400 EFL compositions from 100 university students in Southwestern China and compared them with 200 writing samples from the Louvain Corpus of Native English Essays. Scores of 11 syntactic complexity indices were calculated using the L2 Syntactic Complexity Analyzer. The independent samples t-test was conducted to investigate whether and the extent to which the two groups differed on syntactic complexity indices. The results showed that university EFL students in Southwestern China produce a similar length of linguistic units when compared to native English writers. However, the amount of subordination in EFL writing is significantly less than that of native English writers. For the amount of coordination, the university EFL students produced a lower proportion of coordinate phrases than that of native writers, but the proportion of coordinate sentences is not significantly different between the two groups. Finally, for degree of phrasal sophistication, university EFL students in Southwestern China produce significantly fewer complex nominals than native writers do. The results imply that university students in Southwestern China should write more subordinated sentences and complex nominals, such as nominal clauses, infinitives, or gerunds, in their future EFL writing, instead of writing long sentences just heavily relying on simple coordination.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".