The Theoretical and Research Basis of Co-Constructing Meaning in Dialogue
Bibliographic record
Abstract
de Shazer (1991) introduced a post-structural view of language in therapy in which the participants' sociai interaction determines the meaning of the words they are using. Broader theories of social construction are similar but lack details about the role of language. This article focuses on the observable details of co-constructing meaning in dialogue. Research in psycholinguistics has provided experimental evidence for how speakers and their addressees collaboratively co-construct their dialogues. We review several of the experiments that have demonstrated the influence and importance of the addressee in shaping what the speaker is saying. Building on this research, we present a moment-by-moment three-step grounding sequence in which the speaker presents information, the addressee displays understanding, and the speaker confirms this understanding. We propose that this micro-pattern and its variations are the observable process by which the participants in a dialogue negotiate and co-construct shared meanings.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.084 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".