Multimodal practices for negative assessments as delicate matters: Incomplete syntax, facial expressions, and head movements
Bibliographic record
Abstract
Abstract This paper contributes to the discussion of fuzzy boundaries by investigating negative assessments of the recipient and non-present parties that are syntactically incomplete. Particularly, it explores how the speaker uses syntax and bodily visual conduct to accomplish the delicate action of negatively assessing others and to solicit the recipient to collaboratively complete negative assessments. Based on an examination of approximately 5 h of everyday Mandarin face-to-face conversations, the study shows that incomplete syntax, facial expressions, and head shakes constitute multimodal practices in making negative assessments of the recipient and a non-present third party. Leaving assessments syntactically incomplete and displaying negative evaluative stance through facial expressions such as lip-pursing and eyebrow furrows and head shakes show the speaker’s orientation to the negative assessments as a delicate action. The facial expressions after incomplete syntax demonstrate that participants orient to the hesitation in the delivery of a TCU/turn-in-progress not asproductionproblem, but rather aninteractionalproblem. This study shows that the boundaries of assessment turns may be blurry, and that one assessment may be collaboratively produced by two participants, which exemplifies a specific aspect of weak cesuras and fuzzy boundaries of units and actions in interaction.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".