On stance-taking with one-sided vs. two-sided shoulder lifts in German talk-in-interaction
Bibliographic record
Abstract
Taking a stance toward events, objects, and other persons is fundamental to human interaction. We investigate one specific body movement that is involved in stance-taking in interaction: a shoulder lift, realized as either a one-sided or a two-sided movement. Using multimodal Conversation Analysis, we trace how interactants employ shoulder lifts in different positions within responsive turns in various interaction types in German. This study reveals how the actions to which shoulder lifts contribute are bound to specific turn and sequence positions. We demonstrate how shoulder lifts are used for disclaiming the speaker's accountability or responsibility by framing their turn as non-expandable or non-expansion-worthy, thus curtailing the sequence. Furthermore, the study shows how participants orient to different types of shoulder movements, i.e., lifts with one or with both shoulders, as accomplishing different interactional tasks. By showing that shoulder lifts are a positionally sensitive resource for speakers in building stances, we showcase the potential of conversation analytic and interactional linguistic approaches to further our understanding of multimodal stance-taking 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".