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Record W2267356682 · doi:10.1075/scld.3

Multimodality, Interaction and Turn-taking in Mandarin Conversation

2014· book· en· W2267356682 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudies in Chinese language and discourse · 2014
Typebook
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMultimodalityMandarin ChineseConversationConversation analysisProsodyLinguisticsSyntaxApplied linguisticsAction (physics)Turn-takingPsychologyComputer scienceCommunicationPhilosophy

Abstract

fetched live from OpenAlex

One major feature of conversation is that people take turns to speak. Based on audio and video recordings of naturally-occurring Mandarin conversation, this book explores the role of syntax, prosody, body movements as well as their interplay in turn organization in the temporal unfolding of action and interaction. Adopting the methodology of interactional linguistics, this book offers a fine-grained analysis of the three multimodal resources and the sequential environments in which they appear. It demonstrates that syntax, prosody and body movements not only converge but also diverge in projecting possible turn completion. As one of the few systematic studies of multimodality in Mandarin interaction, this book will be of interest to researchers in Chinese linguistics, interactional linguistics, conversation analysis, and multimodal analysis.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.394
Teacher spread0.332 · 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