An Empirical Study on Pragmatic Transfer in Refusal Speech Act Produced by Chinese High School EFL Learners
Bibliographic record
Abstract
Pragmatic competence plays a very significant role in cross-cultural communication. In Chinese high school, many English teachers focus more on lexical and syntactic aspects of English. The aspect of pragmatics, however, is relatively neglected by high school English teachers. The aim of this research is to investigate pragmatic transfer in refusal speech act made by Chinese high school EFL (English as a Foreign Language) learners. Written DCT (Discourse Completion Test) was used for data collection. Research subjects included three groups: native Chinese speakers (NC), Chinese English learners (CE), and native English speakers (NE). The performance of three groups were compared to find out the differences of refusals made by Chinese and Americans, the characteristics of pragmatic transfer in EFL learners as well as the realtiaonship between pragmtiace transfer and L2 proficiency. Results show that 1) In terms of the frequency of semantic formulas, American speakers liked to use more direct refusal strategies and positive feelings than Chinese speakers. 2) Apparent pragmatic transfer could be found in CE1 and CE3 groups as regards to the frequency of semantic formulas. For instance, both Chinese speakers and EFL learners used address forms while no one in NE group use them. 3) A large amount of pragmatic transfer could be found in the content of refusal strategy of excuse. Statistics show that both NCs and CEs used the similar content as an excuse when giving a rejection. 4) In terms of pragmatic transfer and L2 linguisitic ability, results indicate that the overall tendency of the co-relationship is negative. More pragmatic transfer happened in CE1 group than CE3 group.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".