Multilingual Learners’ Grammatical and Pragmatic Awareness in Chinese EFL Context: From Three Layers of Language Awareness Perspective
Bibliographic record
Abstract
The research was aimed to explore multilingual learners ‘grammatical and pragmatic awareness from perspective of three layers of language awareness: perception, noticing and understanding in Chinese EFL context. The findings reveal that firstly, there exists negative correlation between three layers of grammatical and pragmatic awareness of multilingual learners with low English proficiency; however, there exists positive correlation between three layers of grammatical and pragmatic awareness of multilingual learners with higher proficiency levels. Secondly, on grammatical awareness, there is not a significant difference between Mongolian multilingual learners; on pragmatic awareness, in perception and understanding layer, there exists a significant difference, but in noticing layer, there is not. There were not significant differences between Han Chinese bilinguals and Mongolian multilinguals on grammatical awareness in perception, noticing and understanding as well as perceptional aspect of pragmatic awareness; however, there are significant differences in noticing and understanding. Thirdly, there is a low positive correlation between pragmatic awareness and pragmatic competence of Mongolian learners with different proficiency level. The present research findings indicate the necessity of developing ethnic minority students’ multilingual awareness by training of multilingual teachers and of implementing multilingual pedagogical approaches, and highlight significance of increasing teachers’ language awareness by further development of language teacher education and cultivating learners’ multilingual competence in EFL contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".