Formulaic Language in the Acquisition of L2 Pragmatic Competence in a Community-based Classroom
Bibliographic record
Abstract
Pragmatic formulas have been recognized as linguistic building blocks necessary for successful speech act performance. Current approaches to speech act teaching overlook pragmatic formulas, promoting an incomplete view of pragmatics instruction. This paper reports on the results of a classroom-based study in which a formula-enhanced treatment focusing on both pragmalinguistic and sociopragmatic components of pragmatic ability was tested. Seven students from the Language Instruction for Newcomers to Canada (LINC) program participated in four lessons involving pre-, post- and delayed post-test measures. During the treatment, the students were exposed to target formulas from four interaction contexts. A qualitative utterance analysis was conducted to determine how pragmalinguistic and sociopragmatic abilities of the students evolved after the teaching intervention. Additionally, three expert judges evaluated students’ pragmatic performance. The results indicate that improvements in both pragmalinguistic and sociopragmatic abilities of the students were associated with the use of target-like formulas in their speech acts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".