The Acquisition of “N + that” Appositive Clauses of Chinese EFL Learners: A Corpus-Based Study
Bibliographic record
Abstract
The present research used a corpus-based method to investigate Chinese EFL learners’ acquisition of “N + that” appositive clauses by comparing data from the TECCL corpus of Chinese English learners and the NESSIE corpus of native English speakers. Quantitatively, the number of Ns (nouns) distributed in “N + that” appositive clauses in the TECCL has no significant difference from that in the NESSIE; but results of Chi-square tests revealed that the Ns that are frequently used by Chinese learners in these clauses are significantly different from those used by English native speakers, which may result from the grammatical drills in English teaching and learning in Chinese schools. Qualitatively, Errors occurring in the “N + that” appositive clauses in Chinese EFL learners’ compositions could be classified into two types: Errors in Vocabulary and Errors in Sentence Structure; and the errors could be explained by the Markedness Differential Hypothesis (Eckman, 1977, 1989) and the Interlanguage Theory (Selinker, 1972). This study may bring implications to the teaching and learning/acquisition of English appositive clauses.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".