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Record W2561264090 · doi:10.1075/cld.7.2.03li

Some interactional uses of syntactically incomplete turns in Mandarin conversation

2016· article· en· W2561264090 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

VenueChinese Language and Discourse An International and Interdisciplinary Journal · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConversationMandarin ChineseConversation analysisLinguisticsDisengagement theorySituatedFace (sociological concept)PsychologyComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

In everyday conversation, sometimes a speaker may not complete his/her turn, and the recipients do not treat it as problematic. This paper investigates this type of syntactically incomplete turns (henceforth, SITs) in Mandarin conversation. Specifically, this study examines how SITs are used and constructed through multimodal resources in Mandarin face-to-face conversation. Adopting the methodology of conversation analysis, interactional linguistics, and multimodal analysis, the present study examines 8 hours of everyday Mandarin face-to-face conversation. It shows that the SITs are situated in particular sequential environments and triggered by local contingencies. For example, they are used to accomplish socially and interactionally inappropriate actions and display sensitivity to the recipients’ disengagement from the ongoing talk and the current participation framework. Also, despite the syntactic incompleteness of the SITs, the prosodic and bodily-visual features involved in their production usually indicate possible turn completion.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.343
Teacher spread0.321 · 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