Corpus-Based Error Analysis of Chinese Learners’ Use of High-Frequency Verb Take
Bibliographic record
Abstract
This study investigated the erroneous use of the high-frequency verb TAKE by the Chinese college learners of English as a foreign language (EFL), aiming to identify the similarities and differences between Chinese EFL learners, aimed at finding out more effective ways for the teaching and researching of the high-frequency verbs. Corpus-based Contrastive Interlanguage Analysis and Error Analysis were carried out in the present study, with the subcorpora ST4 and ST6 of CLEC (Chinese Learner English Corpus) as the learner corpora.  The analyses involved the misuse of the verb TAKE by the Chinese EFL learners. The error analysis of TAKE was based on the classification in the corpus CLEC. From the perspective of the overall frequency, the ST6 learners commit fewer errors than the ST4 learners. From the perspective of error types, the ST6 learners and the ST4 learners have much in common. That is, the error types of “wd” and “cc” take up an overwhelming part of all the errors in both corpora. These errors are caused by some interlingual and intralingual factors such as language transfer, overgeneralization, and communication strategy. In comparison, in the process of EFL learning, the non-English majors are interfered by their mother tongue to a larger extend than the English majors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 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".