“You’re putting words in my mouth!”: Interaction as mutual ventriloquation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".