Spontaneous classroom engagement facilitating development of L2 pragmatic competence
Bibliographic record
Abstract
Abstract The question of how to teach toward social, cultural and linguistic L2 pragmatic competence has raised serious challenges ( Kasper, 1997 ). This is more the case for spoken rather than written discourse. As can be expected, the underlying pragmatic implications of spontaneous face-to-face communication naturally constrains the interactional scope and its potential pedagogical application. To address this issue, this naturalistic study explores two key potential contributors to the development of oral pragmatic competence: meta-pragmatic classroom conversational discourse and the course framework supporting that kind of relatively spontaneous interaction. An English for Academic Purposes (EAP) course provided fluency practice protocols, instruction in pragmatic categories, analysis of conversational data and “live” in-class intervention, focusing on meaning and alternate expressions and forms. Those interventions were designed to enhance learners’ ability to self-assess, monitor and expand their interactional repertoires. Part of a larger research project examining principles of pragmatics applied in EAP instruction, this study focuses on data from spontaneous classroom interactions situated within the integrated instructional framework of the course. Results present a range of strategies employed by the instructor consistent with current theoretical models of factors or pedagogical interventions that facilitate development of pragmatic competence.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".