Some Functions of Triadic Dialogue in the Classroom: Examples from L2 Research
Bibliographic record
Abstract
This article argues that triadic dialogue (Lemke, 1990), much criticized in the past, has an important role to play in L2 learning and that its effectiveness should be judged in accordance with the particular pedagogical goals that it is made to serve. Drawing on three recent studies of L2 classrooms in a variety of instructional settings, the article provides illustrative examples of the effective use of different manifestations of triadic dialogue. The teachers in the three studies appear to use triadic dialogue for one or more of the following purposes: (a) a consecutive focus on content and language; (b) a simultaneous focus on content and language; (c) making interaction more dialogic; and (d) encouraging students to exercise their agency as participants. In all contexts, it appears to be critical that teachers attend to both intellectual and affective dimensions of learning in order to create a productive classroom community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".