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Record W4309745547 · doi:10.1080/22041451.2022.2141862

“You’re putting words in my mouth!”: Interaction as mutual ventriloquation

2022· article· en· W4309745547 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

VenueCommunication Research and Practice · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicCultural and Communication Design Research
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChemistryCommunicationPsychology

Abstract

fetched live from OpenAlex

The accusation that someone is putting words in someone else’s mouth can be heard in everyday conversations, but what does this phenomenon reveal about the ways human beings communicate? This paper aims to show that it is useful to view putting words in someone’s mouth as a form of ventriloquation. By theorising this phenomenon, this paper explicates how people discover a version of what they said in their interlocutors’ mouths, and in turn react to these ventriloquations. Since this phenomenon is especially visible in conflict situations, this paper demonstrates the value of using a ventriloquial lens to study human interactions through a detailed analysis of a public dispute and a conflict mediation session. Thus, this paper shows how this lens can be used to gain insight into the communicative constitution of conflict as well as its resolution. More broadly, it proposes to conceive of interaction as a process of mutual ventriloquation and highlights the methodological, ethical, and political implications of this analytical move.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.791
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.414
GPT teacher head0.472
Teacher spread0.058 · 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