Production and Comprehension Aspects of Pragmatic Competence in an Immersive Language Program
Bibliographic record
Abstract
Since pragmatic ability appears to be a vital skill for social transactions, Bardovi-Harlig and Mahan-Taylor (2003) have argued for the inclusion of explicit instruction in pragmatics within general language instruction. However, their study adopts a speech-act framework that does not differentiate between pragmatic production and pragmatic comprehension . L1 learners develop a comprehension stage before producing appropriate utterances (Berk, 2012), and it may be that L2 learners do likewise. To advance pedagogy, this paper addresses four research questions within the context of a residential, immersive language program in an EFL setting: 1) Is there any relationship between language proficiency and the production aspect of pragmatic competence? 2) Is there any relationship between language proficiency and the comprehension aspect of pragmatic competence? 3) To what extent does an immersive language program lead to the development of the production aspect of pragmatic competence? and 4) To what extent does an immersive language program lead to the development of the comprehension aspect of pragmatic competence? Japanese first-year college students (n=30) were assessed through three instruments at the start of a one-year language immersion program: TOEFL PBT; a 32-item pragmatic production test (Bardovi-Harlig, 2009); and a 58-item pragmatic comprehension test (Taguchi, 2007, 2008, 2012). The correlation between language proficiency and pragmatic production, as well as between language proficiency and pragmatic comprehension, was computed through Pearson correlation coefficient. Fifteen of the subjects thereupon participated in an intensive language program. At the end of the academic year, all 15 subjects took the pragmatic production and comprehension tests again (post-tests). The findings of the one-year longitudinal study on the efficacy of language instruction in an immersive language program, and its relation to both production and comprehension aspects of pragmatic competence, is demonstrated. Language proficiency had a positive correlation with gains in both pragmatic production and pragmatic comprehension. Also, language instruction, even without specifically addressing pragmatic instruction, had a significant effect on developing both pragmatic production and pragmatic comprehension.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".