Providing Semi-private Feedback on a Shared Public Screen by Controlling Presentation Onset
Why this work is in the frame
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Bibliographic record
Abstract
We describe a novel technique to provide semi-private feedback on a shared public screen. The technique uses a no-onset presentation that takes advantage of perceptual limitations in human vision to avoid alerting other users to feedback directed at one individual user by suppressing the sudden onset of the feedback. Three experiments evaluated the effectiveness of the technique and appropriate timing parameters and alternatives for presentation onset. Our experiments indicated that an 80 ms no-onset presentation allows participants to interpret information directed to them with over 90% accuracy, but their ability to interpret simultaneously presented information intended for others will be close to random chance. The technique initially camouflages the information being presented by overlaying additional visual elements and then removes those elements to reveal only the elements encoding the information being presented. We discuss applications for the technique, including classroom clicker usage, which was our original motivation for the study.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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