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Record W2102450237 · doi:10.1109/mcg.2015.52

Understanding Digital Note-Taking Practice for Visualization

2015· article· en· W2102450237 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Computer Graphics and Applications · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of Calgary
FundersInstitut national de recherche en informatique et en automatique (INRIA)
KeywordsComputer scienceVisualizationVariety (cybernetics)Reflection (computer programming)Digital contentDigital libraryWorld Wide WebData visualizationHuman–computer interactionData scienceMultimediaArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.583
GPT teacher head0.490
Teacher spread0.094 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it