The Contribution of Gestures to the Semantics of Non-Canonical Questions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The symbolic gesture MAT (mano a tulipano) used by native speakers of Italian characterizes non-canonical wh questions when used both as a co-speech and pro-speech gesture. MAT can be executed with either a fast tempo contour or a slow tempo contour. Tempo is semantically significant: descriptively, a fast tempo characterizes a biased but information-seeking non-canonical question; a slow tempo characterizes a rhetorical non-canonical question. I argue that the fast contour is the default tempo of MAT and that it brings about a biased interpretation. Slowing down the movement occurs when the feature [slow] is added: the semantic contribution of this feature is to add the presupposition that the question is resolved in the conversational context, resulting in the rhetorical interpretation of the question.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it