Gaze Direction Signals Response Preference in Conversation
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we examine gaze direction in responses to polar questions using both quantitative and conversation analytic (CA) methods. The data come from a novel corpus of conversations in which participants wore eye-tracking glasses to obtain direct measures of their eye movements. The results show that while most preferred responses are produced with gaze toward the questioner, most dispreferred responses are produced with gaze aversion. We further demonstrate that gaze aversion by respondents can occasion self-repair by questioners in the transition space between turns, indicating that the relationship between gaze direction and preference is more than a mere statistical association. We conclude that gaze direction in responses to polar questions functions as a signal of response preference. Data are in American, British, and Canadian English.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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