Promoting Feature Awareness by Leveraging Collaborators' Usage Habits in Collaborative Editors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Users often rely on their collaborators to find relevant application features by observing them "over the shoulder" (OTS), usually in a synchronous co-located setting. However, as remote work settings have become more common, users can no longer rely on such in-person interaction with collaborators. Therefore, we investigate designs that help the user become aware of relevant features based on collaborators' feature usage habits. We created five design concepts as video prototypes which varied in five design dimensions: number of active collaborators, number of shared documents, specificity of comparison, user involvement, and goal of the feature awareness. Interviews (N=18) probing the design concepts indicate that collaborator-based feature awareness would be valuable for discovering novel features and producing a consistent style across the shared document, but some users may feel micromanaged or self-conscious. We conclude by reflecting on and expanding our design space and discussing future design directions supporting remote OTS learning.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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