An Empirical Study of Pragmatic Input to Chinese EFL Learners
Bibliographic record
Abstract
Pragmatic competence has attracted increasing attention and research in SLA. Most studies, however, are product-oriented rather than input-oriented. A study of the learning targets without the support of findings from the study of the input to the learners would have no sound and reasonable basis. In view of this, we set out to explore the essentials of pragmatic input in structured or unstructured teaching contexts. An experiment was done to show whether pragmatic input to Chinese EFL learners in language teaching and learning is quantitatively and qualitatively sufficient. Findings show that the Chinese EFL learners are in great shortage of pragmatic knowledge and consequently lag behind in the development of pragmatic competence and such shortage is attributable to lack of inadequate pragmatic input. Taking into consideration the results of the present study and those yielded from related research, we propose that the pragmatic input to the learners should be enriched, contextualized and offered explicitly, and the Chinese learners of English are expected to play a more active role in acquiring necessary pragmatic input from all sources in the learning context of modern China.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".