Soft-Constraints to Reduce Legacy and Performance Bias to Elicit Whole-body Gestures with Low Arm Fatigue
Why this work is in the frame
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Bibliographic record
Abstract
Participant biases can influence proposed gestures in elicitation studies. There is a legacy bias from previous experience with, or even knowledge of, existing input devices, interfaces, and technologies. There is also a performance bias, where the artificial study setting does not encourage consideration of long-term aspects such as fatigue. These biases make it especially difficult to uncover gestures appropriate for whole-body gestural input. We propose using soft constraints to correct for legacy and performance biases by penalizing physical movements. We use wrist weights as a soft constraint to elicit whole-body gestures with low arm fatigue. We show soft constraints encourage a wider range of gestures using subtler arm movements or alternate body parts and lower consumed endurance for arm movements.
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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 | 0.000 | 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 | 0.001 |
| 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