The Role of Grammatical Competence in Shaping Pragmatic Performance: A Study of L2 Learners’ Requests in English
Bibliographic record
Abstract
This study was a comparative analysis of data collected from two participants: a native speaker of English and a second language (L2) learner of English. The L2 learner was a female Arab student learning English at level 3 in an intensive English program at an American university. The native speaker was a female undergraduate student majoring in International Studies in her second year. The data consisted of responses to various situations in which participants made written requests to fictional interlocutors. The situations featured different contexts and degrees of imposition, and the statuses of the interlocutors were alternated in each situation to elicit different responses. The purpose of the study was to compare the L2 learner's responses to those of a native English speaker in terms of form and function and to assess whether these aspects play a role in making and perceiving requests. Another aim of the analysis was to investigate the contribution of the L2 learner’s level of linguistic and grammatical competence to her level of interlanguage pragmatics in making requests. The results revealed that the learner’s level of linguistic competence influenced her interlanguage pragmatics in both form and function. In conclusion, it is recommended that more interlanguage analyses should be conducted to examine the different proficiency levels of L2 learners and how these contribute to their development of interlanguage pragmatics. Also, the study provides implications for second language acquisition and English language 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".