Post-meeting Curation of Whiteboard Content Captured with Mobile Devices
Bibliographic record
Abstract
The traditional dry-erase whiteboard is a ubiquitous tool in the workplace, particularly in collaborative meeting spaces. Recent studies show that meeting participants commonly capture whiteboard content using integrated cameras on mobile devices such as smartphones or tablets. Yet, little is known about how people curate or use such whiteboard photographs after meetings, or how their curation practices relate to post-meeting actions. To better understand these post-meeting activities, we conducted a qualitative, interview-based study of 19 frequent whiteboard users to probe their post-meeting practices with whiteboard photos. The study identified a set of unmet design needs for the development of improved mobile-centric whiteboard capture systems. Design implications stemming from the study include the need for mobile devices to quickly capture and effortlessly transfer whiteboard photos to productivity-oriented devices and to shared-access tools, and the need to better support the extraction of whiteboard content directly into other productivity application tools.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".