A Case Study on the Relationship between Individual Differences and English Pragmatic Competence of Non-English-Major Chinese Postgraduates
Bibliographic record
Abstract
In the field of pragmatics, a lot of research has been done concerning the pragmatic failures committed by college students. Little research is oriented to the non-English-major postgraduates (NEMP). For that reason, this paper attempts to study the pragmatic competence of NEMP, especially the relationship between pragmatic competence and their individual differences, such as gender differences and differences among different majors, age and working experience. A questionnaire was administrated to 115 non-English-major postgraduates in East China University of Technology (ECUT) in China. The questionnaire consists of an English pragmatic competence test with 25 multiple choices, which was selectively taken from He Ziran’s study (1988). By using both quantitative and qualitative analysis, the study offers the following findings: the general level of NEMP’s pragmatic competence is quite low at present, but with their developing cultural awareness, they do a better job in sociopragmatic competence than that in pragmalinguistic competence; there are significant differences between different genders and their pragmatic competence, with the females’ pragmatic competence higher than males’, while the differences among majors, age and working experience are not so significant in the participants’ English pragmatic competence. The paper also offers some suggestions for postgraduates’ English teaching: using different teaching methods should be considered; Input of English pragmatic knowledge in proper contexts and making best use of Internet should be increased as well; the postgraduates’ individual differences should also be treated appropriately in English teaching.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".