Is Chinese English Majors’ Tendency to Use Modal Sequences Better with the Passing of Their College Campus Time？
Bibliographic record
Abstract
Based on the result of the trends of modal sequences in Chinese English majors’ argumentation, this research focuses on the relationship between English majors’ tendency to use modal sequences and their college campus time. The paper reveals that the tendency to use modal verbs is not related to their college campus time, and that epistemic and deontic modality to uses are not related to their college campus time, either. This study offers reference to the understanding of how Chinese students acquire modal verbs and gives suggestions for modal verb teaching which are the following: (1) We should bear in mind when compiling textbooks that more exposure to epistemic modal verbs with euphemism modality for students is needed in early senior high textbooks; (2) Native speakers’ tendency to use modal verbs should be explicitly clarified in class; (3) native speakers tend to use should be consciously presented both in and after class; (4) The proper pragmatic meaning of modal verbs, the basic value view and social philosophy of Anglo-American Culture involved as well as the differences in cultural tradition and value between East and West should be underlined in English modal verbs teaching.
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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 | 0.003 |
| 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 | 0.001 |
| 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 | 0.000 |
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".