Collaborative construction of turn constructional units in responsive positions of question-answer sequences in Mandarin conversation
Bibliographic record
Abstract
Abstract This paper explores the features and interactional functions of collaboratively constructed TCUs (CCTs) in responsive positions of question-answer sequences in Mandarin daily conversations. Adopting the methodologies of Conversation Analysis, Interactional Linguistics and Multimodal Analysis, the study explores the sequential features of the CCTs and bodily-visual resources co-occurring with the CCTs, such as gaze orientations and gestures. Two categories have been identified based on the participants’ roles in the question-answer sequences. First, the answerer initiates the response to the question, and the questioner collaboratively completes the response. The analysis shows that the questioners are not conveying the action of answering the question but assuming the answer to the question. Second, one answerer initiates the response to the question, and another one collaboratively completes the response. The data demonstrates that this type of CCTs usually involves the two question-recipients with more or less equal epistemic access to the referent.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".