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Record W2077773484 · doi:10.1080/0163853x.2014.883730

Effect of Dialogue on Demonstrations: Direct Quotations, Facial Portrayals, Hand Gestures, and Figurative References

2014· article· en· W2077773484 on OpenAlexaff
Janet Beavin Bavelas, Jennifer Gerwing, Sara Healing

Bibliographic record

VenueDiscourse Processes · 2014
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGestureLiteral and figurative languageFace (sociological concept)LinguisticsReferentPsychologyNonverbal communicationClass (philosophy)Computer scienceCommunicationArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Demonstrations (e.g., direct quotations, conversational facial portrayals, conversational hand gestures, and figurative references) lack conventional meanings, relying instead on a resemblance to their referent. Two experiments tested our theory that demonstrations are a class of communicative acts that speakers are more likely to use in dialogue than in monologue. We compared speakers' rates of demonstrations in face-to-face dialogues, telephone dialogues, and monologues into a handheld microphone or recorder. Experiment 1 confirmed that the proportions of speakers' direct quotations and facial portrayals were (1) significantly higher in the two dialogue conditions than in the monologue condition and (2) not significantly different in the two dialogue conditions. Experiment 2 found the same patterns for the rates of figurative references and hand gestures. In both experiments, regressions confirmed that the increase in demonstrations in dialogues was independent of any effect of visibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.355
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations53
Published2014
Admission routes1
Has abstractyes

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