When Gestures also Argue: Multimodal Viewpoint Shift as a Rhetorical Strategy in American Political Talk Shows
Bibliographic record
Abstract
Drawing on Mental Spaces Theory and Conceptual Blending Theory in Cognitive Linguistics, we examine the interaction between speech and co-speech gestures in The Daily Show with Trevor Noah. Statistical analysis shows that viewpoint shift in the self-built multimodal corpus, generally following the pattern: Base Space->News Narrative Space(->Base space)->Source Viewpoint Space(+)(->Base space), is significantly related to the use of verbal markers and gestural types. Moreover, multimodal viewpoint shift in political talk shows can achieve such rhetorical functions as enhancing ironic effects, solidifying and highlighting opposing positions, and simplifying political issues. Since verbal markers and gestures can mobilize the audience’s embodied experience by primarily activating mental images and motor programs, we claim that mental simulation and perspective taking play a pivotal role in the cognitive processing of multimodal viewpoint shifts by promoting viewpoint alignment between the audience and the host.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".