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Record W4401533749 · doi:10.1075/slsi.36.07gol

Verbal and bodily practices for addressing trouble associated with embodied moves in game play

2024· book-chapter· en· W4401533749 on OpenAlexaff
Andrea Golato, Emma Betz, Carmen Taleghani‐Nikazm, Veronika Drake

Bibliographic record

VenueStudies in language and social interaction · 2024
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEmbodied cognitionPsychologyCognitive scienceCommunicationCognitive psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract We provide a first systematic account of how interactants manage trouble that is not localizable in talk but rather in embodied conduct in table-top game play. Interventions targeting embodied problems have been termed “remedial actions” ( Lerner and Raymond 2021 ) or “remedies” ( Arminen and Auvinen 2013 : 19). Focusing on game-playing interactions in German and English, we show that remedial actions addressing a coparticipant’s prior embodied move, or the absence of a move, as troublesome can take one of three different forms: they can be embodied, verbal, or a combination thereof. We show a systematic link between the form of remedial action and the type of trouble addressed: Remedial actions that are exclusively embodied address deviations from shared playing practices, typically involving problems with game piece placement. Exclusively verbal remedial actions address violations of codified game rules such as premature moves. Remedial actions that combine verbal and embodied resources target established practices or formal rules but accomplish additional actions, for example doing teaching or reproaching. By highlighting the systematic interplay between talk and embodiment, our study contributes to a new, multimodal perspective in Interactional Linguistics.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.183
GPT teacher head0.411
Teacher spread0.228 · 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 designQualitative
Domainnot available
GenreOther

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

Citations1
Published2024
Admission routes1
Has abstractyes

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