An Error Analysis of Coordinating Conjunction Misuse in Chinese ESL Learners’ Writings: A Corpus-based Approach
Bibliographic record
Abstract
Coordinating conjunctions, which are more recurrently used words than the other words in English, are not as easily well-acquired by Chinese students as they are intuitively used by native speakers. Yet, insufficient attention has been drawn to the study of coordinating conjunctions, which often leads to great difficulties in the acquisition of coordinating conjunctions for Chinese English as a second language learners. The present thesis has selected the three coordinating conjunctions of higher frequency, and, but and or as the target words in the research to analyze the misuse of these words in Chinese ESL learners’ writings of English under the theory of Error Analysis.The thesis established two corpora: Learner Corpus with 21 theses of Chinese graduate students of English major, downloaded from China National Knowledge Infrastructure (CNKI); Control Corpus with 22 theses from Proquest-PQDT. All the dissertations were essay writings about linguistics and were randomly selected. It was shown that there existed a minor gap in the frequency usage of coordinating conjunctions and, but and or between Chinese students of English major and native English speakers. However, the usages of SIA (Sentence-initial And) and SIB (Sentence-initial But) were strongly different between Chinese ESL learners and native English speakers. The conclusions above could serve as pedagogical references for English teachers in China so that Chinese ESL learners could come close to native speakers in terms of the usages of the three coordinating conjunctions and, but and or.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".