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Record W3164427237 · doi:10.1007/s10919-021-00374-2

Paralinguistic Features Communicated through Voice can Affect Appraisals of Confidence and Evaluative Judgments

2021· article· en· W3164427237 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Nonverbal Behavior · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsParalanguagePersuasionPsychologyAffect (linguistics)CognitionIntonation (linguistics)Social psychologyCognitive psychologySincerityCommunicationLinguistics

Abstract

fetched live from OpenAlex

This article unpacks the basic mechanisms by which paralinguistic features communicated through the voice can affect evaluative judgments and persuasion. Special emphasis is placed on exploring the rapidly emerging literature on vocal features linked to appraisals of confidence (e.g., vocal pitch, intonation, speech rate, loudness, etc.), and their subsequent impact on information processing and meta-cognitive processes of attitude change. The main goal of this review is to advance understanding of the different psychological processes by which paralinguistic markers of confidence can affect attitude change, specifying the conditions under which they are more likely to operate. In sum, we highlight the importance of considering basic mechanisms of attitude change to predict when and why appraisals of paralinguistic markers of confidence can lead to more or less persuasion.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.074
GPT teacher head0.444
Teacher spread0.369 · 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