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Record W2145275787 · doi:10.1177/1461445609346922

Agreement, acknowledgment, and alignment: The discourse-pragmatic functions of hao and dui in Taiwan Mandarin conversation

2010· article· en· W2145275787 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

VenueDiscourse Studies · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Toronto
FundersNational Science Council
KeywordsUtteranceConversationPolitenessRelevance theoryMandarin ChineseLinguisticsPsychologyTurn-takingConversation analysisRelevance (law)Discourse analysisComputer scienceCommunicationPhilosophy

Abstract

fetched live from OpenAlex

This study draws on Relevance Theory (Sperber and Wilson, 1986/1995), Conversation Analysis (Sacks et al., 1974), and Politeness Theory (Brown and Levinson, 1987) in investigating a full range of discourse functions for hao and dui with reference to recurrent patterns, distributions, and forms of organization in a large corpus of talk. Special emphasis is placed on a comparison of hao and dui in combination with a small subset of discourse particles: in particular hao/hao le/ hao la/hao a/hao ba and dui/dui a/dui le in spoken discourse. We find that both of the markers signal special sequential relatedness in talk and carry information which is relevant in determining the boundaries of conversational exchange. However, in interaction hao is used for expressing acceptance of the other speaker’s move or act, whereas dui conveys acknowledgment of the propositional content of the utterance produced by the other speaker.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.998

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 studies0.0000.002
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 head0.036
GPT teacher head0.324
Teacher spread0.288 · 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