Effects of Classroom Instruction on the Development of L2 Interactional Resource for Joint Stance Taking: Use of Japanese Interactional Particle<i>Yo</i>in Spontaneous Peer Conversation
Bibliographic record
Abstract
Abstract The present study examines the development of L2 interactional competence (Hall et al. 2011) focusing on JFL learners’ use of a Japanese interactional particle yo in spontaneous conversation with native speaker peers. More specifically, this study aims to investigate the instructional effectiveness for the learners’ ability to use the particle yo as an epistemic marker for joint stance taking (Morita 2015, 2018) in interaction. The study implements pragmatics-focused instruction, incorporating awareness-raising and conversational activities, in a third-semester Japanese class for one semester. Qualitative analysis focused on the learners’ ability to deploy yo in activity-relevant participation including an assessment activity (Goodwin and Goodwin 1992) and evidence for their use of yo that extends beyond the instructional content. The findings revealed that the instructed learners increasingly became able to use yo in various stance-indexing activities such as assessment, news telling, highlighting epistemic gap between participants toward the ongoing topic. Learners’ emerging interactional competence reflects their ability to use yo as an interactional resource for co-constructing stance and intersubjectivity as they engage in conversation with NS peers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".