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Record W4409864251 · doi:10.3389/fpsyg.2025.1509988

On stance-taking with one-sided vs. two-sided shoulder lifts in German talk-in-interaction

2025· article· en· W4409864251 on OpenAlexafffund
Emma Betz, Alexandra Gubina

Bibliographic record

VenueFrontiers in Psychology · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Waterloo
FundersLeibniz-GemeinschaftUniversity of Waterloo
KeywordsConversation analysisPsychologyShouldersGermanConversationTurn-takingFraming (construction)Cognitive psychologyGazeLift (data mining)CommunicationSocial psychologyLinguisticsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.373
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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