Characteristics of Pronoun “Who and Its Concordance” in Chinese College Students’ English Narrative Writing from the Perspective of Corpus-Based Method - A Case Study of Series of Compositions of “The Most Unforgettable Person I Ever Know”
Bibliographic record
Abstract
Writing is one of productive skills and a way of conveying information considered to be the most complex and the most challenging skill for EFL English learners to acquire, hence many studies have been conducted on the revelation of the characteristic of writings of EFL learners and how to improve them. Among them, pronoun study has attracted extensive interest and become a hot spot in the second language acquisition and contrastive linguistics. Taking a series of compositions of “The most unforgettable person I ever know” as subject, this study is devoted to reveal the characteristics of “who and its concordance” in Chinese college students` English narrative writing from the perspective of corpus-based method. Result of contrastive analysis of writing from 630 college students of 3 colleges in the past 6 years shows: 1) As a whole, WIC can be regarded as common words for Chinese college students but the distribution of individual word is relatively disproportionate-“who” attracts far more attention while the other 4 words attract little or no attention. 2) In terms of sentence type, the distribution of the 5 types is imbalance with too little use of adverbial clause and too much of attributive clauses. In addition, the learners are used to utilizing simple and identical sentence structure and some of them are highly repetitive. 3) The use frequency of WIC of individual student shows that the majority numbers of learners have formed the habit of using them to depict interpersonal relationship and their distribution is unbalanced too. 4) As to the clusters, the learners have formed the habit of preference use of “who” as a relative pronoun above all. And some clusters are highly identical and simple. 5) To sum up, the majority of learners in this study can employ WIC consciously in their writing, but their usages are confined to simple words and structures. Therefore, the learner`s comprehensive competence of integrated employment of WIC should be improved.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".