The Effect of Chinese Learners’ Modality Converting Competence on Their EFL Output System
Bibliographic record
Abstract
This research introduces modality converting competence into foreign language aptitude composition and makes a diachronic study on the relationship between the internal variables for Chinese learners’ EFL written output system and their modality converting competence. Through multiple correlation analysis and multiple regression modeling, it could be concluded that compared with the variables of the same modal, the variables of modality converting competence were more correlated to the variables of EFL written system and the latter could respectively account for 72.2%, 57.8%, 65.9% and 67.0% of the variation of the written system variables W/T, DC/T. S/T and LC/T. As the advantage of aptitude, modal converting competence might produce influence upon learners’ syntactic and lexical system complexity in their written output via information processing process. The significance of this research lies in that it might provide more practical approaches for the realization of aptitude treatment interaction (ATI).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".