A Survey of Acquisition of Transferred Negation of English Syntax With Reference to Senior High School TEFL in Mainland China
Bibliographic record
Abstract
This study investigates the learning situation of senior high school students in mainland China about acquisition of transferred negation of English sentence structure. Through translation test and questionnaire investigation, a survey is made about this language learning phenomenon among 173 students. From the study it is found that students mainly have problems with the typical kind of transferred negation sentences, the negation caused by the predicative in the matrix clause, the negation of because-clause, the negation of infinitive structure or the objective complements and so on. The language foundation of English has no direct relation to whether students are familiar with transferred negation structure. There exists a very clear difference between students from different schools for some sentences and students from the same class tend to make mistakes in the same kind of transferred negation sentences. Female students have an advantage over male students in the translation test. Some factors like the habits of Chinese expression have great effects on students’ understanding of the structure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".