The Investigation of Pragmatic Transfer in the Speech Act of Congratulations by Punjabi EFL Learners
Bibliographic record
Abstract
This paper investigates pragmatic transfer in Punjabi EFL learners’ realization of the speech act of congratulations. For this purpose, 120 participants were asked to take part in the study, who were divided into four groups having 30 participants in each group: 30 native English (NE) speakers, 30 Punjabi EFL learners of the elite class, 30 Punjabi EFL learners of the middle class and 30 Punjabi EFL learners of the lower class. To elicit the data, a DCT having 12 situations based on social power and distance was used and for perception data, these situations were analyzed on the basis of four contextual variables (degree of familiarity, power, difficulty, and obligation in expressing congratulations) in the form of SRQ. The criteria for the analysis of negative pragmatic was set on the differences found in the responses of the participants and the positive pragmatic transfer was based on the similarities between the responses of the participants. The data was coded and analyzed under the taxonomy of congratulations proposed by Elwood (2004). The results of the study indicated the presence of negative pragmatic transfer in three different strategies of congratulation (IFID, OOGW and Overlapped) and the remaining of the strategies indicated the existence of positive pragmatic transfer. The findings of the study show the cultural influence on the use of congratulation strategies by Punjabi EFL learners. The results of the study might be pedagogical significance and could prove helpful for policymaker and syllabus designer as well within Pakistani context.
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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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".