A Comparative Study of Chinese EFL Undergraduates’ Pragmatic Competence in English Letter Writing Between Urban and Suburban Universities
Bibliographic record
Abstract
In this era of globalization, pragmatic competence plays a vital role in cross-cultural communication. The objective of this study is to investigate whether location is a key factor influencing Chinese EFL undergraduates’ pragmatic competence in English letter writing by comparing urban and suburban universities. This study adopted a descriptive research design. The samples of the study were 450 Chinese college students, with 225 from a university located in an urban city (Guangzhou) and another 225 from a university located in a suburban city (Yiyang). All the participants in this study took an English letter writing test and their writings were analyzed from the perspectives of choice of vocabulary, grammar, syntax and organization. The findings from quantitative data indicated that the overall pragmatic competence of the students from an urban university was better than that of the students from a suburban university. Specifically, there was a significant difference in the overall pragmatic competence, choice of vocabulary, grammar, syntax between an urban university and a suburban university, whereas there was no significant difference in organization. Pedagogically, the findings suggest that pragmatic competence and learning environment should be taken into consideration and lecturers could adopt flexible and feasible approaches applicable to students living in different parts of the world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".