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Record W2897520771 · doi:10.1075/gest.00012.bav

Some pragmatic functions of conversational facial gestures1

2018· article· en· W2897520771 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

VenueGesture · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsFraser HealthUniversity of Victoria
Fundersnot available
KeywordsGesturePsychologyFacial expressionContext (archaeology)Face (sociological concept)Nonverbal communicationLinguisticsHead (geology)CommunicationPhilosophyHistory

Abstract

fetched live from OpenAlex

Abstract Conversational facial gestures are not emotional expressions ( Ekman, 1997 ). Facial gestures are co-speech gestures – configurations of the face, eyes, and/or head that are synchronized with words and other co-speech gestures. Facial gestures are the most frequent facial actions in dialogue, and the majority serve pragmatic (meta-communicative) rather than referential functions. A qualitative microanalysis of a close-call story illustrates three pragmatic facial gestures in their macro- and micro-context: (a) The narrator’s thinking faces ( Goodwin & Goodwin, 1986 ) occurred as the narrator was getting started, and they accompanied verbal collateral signals of delay, such as “uh” or “um”. (b) The narrator pointed at his hand gestures with his head and eyes ( Streeck, 1993 ), drawing the addressee’s attention to depictions that would later be crucial to the close call. (c) The meta-communicative functions of smiles included marking the narrator’s description of danger as ironic or humorous, hinting at key elements, and acknowledging errors.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.269
Teacher spread0.240 · 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