Understanding Digital Note-Taking Practice for Visualization
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
We present results and design implications from a study of digital note-taking practice to examine how visualization can support revisitation, reflection, and collaboration around notes. As digital notebooks become common forms of external memory, keeping track of volumes of content is increasingly difficult. Information visualization tools can help give note-takers an overview of their content and allow them to explore diverse sets of notes, find and organize related content, and compare their notes with their collaborators. To ground the design of such tools, we conducted a detailed mixed-methods study of digital note-taking practice. We identify a variety of different editing, organization, and sharing methods used by digital note-takers, many of which result in notes becoming "lost in the pile''. These findings form the basis for our design considerations that examine how visualization can support the revisitation, organization, and sharing of digital notes.
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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 | 0.000 | 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) | 0.000 | 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