Is It Rude Language? Children Learning Pragmatics Through Visual Narrative
Bibliographic record
Abstract
There has been an upsurge of interest in teaching second/foreign language (L2) pragmatics in recent years, but much of this effort has been targeted at adult learners. This article introduces small-scale informal instruction exploring the pragmatic development of 9-year-olds in Tokyo, facilitated through dialogic in- tervention on pragmatics using the visual presentation of narratives. Although the instruction took place in an English as a foreign language (EFL) context, the same dialogic approach is relevant to ESL in Canada and elsewhere, as picture books enrich narratives, visually mediating the context of language use in a manner comprehensible and captivating to young learners. The learners’ pragmatic development was scaffolded dialogically through instructional materials doubling as teacher-based assessments, including formality judgment tasks, discourse completion tasks, and student-generated visual discourse completion tasks, assessed through predesigned rubrics and written reflections by the teacher. Video-recorded data showed that repeated visual assistance provided by the teacher and peers led to enhanced pragmatic awareness and metapragmatic judgments of the relative levels of formality and politeness of the target pragmatic formulas. However, with little L2 exposure, these learners were often unable to produce newly introduced expressions and failed to match the demands of the context with appropriate language choices during this isolated series of instructional events.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 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".