Multimodal Assemblies for Prefacing a Dispreferred Response: A Cross-Linguistic Analysis
Bibliographic record
Abstract
In this paper we examine how participants' multimodal conduct maps onto one of the basic organizational principles of social interaction: preference organization - and how it does so in a similar manner across five different languages (Czech, French, Hebrew, Mandarin, and Romanian). Based on interactional data from these languages, we identify a recurrent multimodal practice that respondents deploy in turn-initial position in dispreferred responses to various first actions, such as information requests, assessments, proposals, and informing. The practice involves the verbal delivery of a turn-initial expression corresponding to English 'I don't know' and its variants ('dunno') coupled with gaze aversion from the prior speaker. We show that through this 'multimodal assembly' respondents preface a dispreferred response within various sequence types, and we demonstrate the cross-linguistic robustness of this practice: Through the focal multimodal assembly, respondents retrospectively mark the prior action as problematic and prospectively alert co-participants to incipient resistance to the constraints set out or to the stance conveyed by that action. By evidencing how grammar and body interface in related ways across a diverse set of languages, the findings open a window onto cross-linguistic, cross-modal, and cross-cultural consistencies in human interactional conduct.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".