Situated willingness to communicate in an L2: Interplay of individual characteristics and context
Bibliographic record
Abstract
Recently, situated willingness to communicate (WTC) has received increasing research attention in addition to traditional quantitative studies of trait-like WTC. This article is an addition to the former but unique in two ways. First, it investigates both trait and state WTC in a classroom context and explores ways to combine the two to reach a fuller understanding of why second language (L2) learners choose (or avoid) communication at given moments. Second, it investigates the communication behavior of individuals and of the group they constitute as nested systems, with the group as context for individual performance. An interventional study was conducted in a class for English as a foreign language (EFL) with 21 students in a Japanese university. During discussion sessions in English over a semester in which Initiation–Response–Feedback (IRF) patterns were avoided to encourage students to initiate communication, qualitative data based on observations, student self-reflections, and interviews and scale-based data on trait anxiety and WTC were collected. The analyses, which focused on three selected participants, revealed how differences in the frequency of self-initiated turns emerged through the interplay of enduring characteristics, including personality and proficiency, and contextual influences such as other students’ reactions and group-level talk–silence patterns.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".