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Record W1984066794 · doi:10.1080/0163853x.2013.778168

Influence of Role-Switching on Phonetic Convergence in Conversation

2013· article· en· W1984066794 on OpenAlexfundno aff
Jennifer S. Pardo, Isabel Cajori Jay, Risa Hoshino, Sara Maria Hasbun, Chantal Sowemimo-Coker, Robert M. Krauss

Bibliographic record

VenueDiscourse Processes · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersAcadia University
KeywordsConversationDominance (genetics)AccommodationPsychologyConvergence (economics)Speech recognitionContrast (vision)Computer scienceCognitive psychologyCommunicationArtificial intelligence

Abstract

fetched live from OpenAlex

The current study examined phonetic convergence when talkers alternated roles during conversational interaction. The talkers completed a map navigation task in which they alternated instruction Giver and Receiver roles across multiple map pairs. Previous studies found robust effects of the role of a talker on phonetic convergence, and it was hypothesized that role-switching would either reduce the impact of role or elicit alternating patterns of role-induced conversational dominance and accommodation. In contrast to the hypothesis, the initial role assignments induced a pattern of conversational dominance that persisted throughout the interaction in terms of the amount of time spent talking—Original Givers dominated amount of time talking consistently, even when they acted as Receivers. These results indicate that conversational dominance does not necessarily follow nominal role when roles alternate, and that talkers are influenced by initial role assignment when making acoustic–phonetic adjustments in their speech.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.339
Teacher spread0.323 · 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

Citations58
Published2013
Admission routes1
Has abstractyes

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