A Contrastive Analysis of Chinese College English Syllabus Word List With a General Service List of English Words and Academic Word List
Bibliographic record
Abstract
Chinese College English Syllabus Word List issued by Chinese Ministry of Education has always been instructing Chinese college English teaching for about 14 years. Now Chinese college English teaching is focusing more and more on learners and to satisfy learners’ needs, Chinese college English teaching is turning from teaching English for general purposes to for academic purposes. To investigate whether Chinese College English Syllabus Word List can meet the vocabulary requirements of academic learning of Chinese college learners or not, a contrastive analysis between Chinese College English Syllabus Word List and A General Service List of English Words and Academic Word List is made via the software, Range. The results show that Chinese College English Syllabus Word List (level 1 to 4) can accord with A General Service List of English Words in word families, but has a low coverage in word types in Academic Word List and most words in Chinese College English Syllabus Word List (level 5 to 6 & level after 6) are not in A General Service List of English Words or Academic Word List. Thus, it can be concluded that Chinese College English Syllabus Word List (level 1 to 4) is made up of high frequent words but not academic words while Chinese College English Syllabus Word List (level 5 to 6 & level after 6) is neither high frequent word list nor academic word list. It is proposed that Chinese College English Syllabus Word List must be revised to meet the requirements of academic learning of Chinese college learners.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".