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Record W3128783651 · doi:10.1109/vis47514.2020.00040

Gaze-Driven Links for Magazine Style Narrative Visualizations

2020· article· en· W3128783651 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceNarrativeGazeHuman–computer interactionModalitiesVisualizationReading (process)Style (visual arts)Eye trackingComprehensionInformation visualizationReading comprehensionMultimediaWorld Wide WebArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

Magazine Style Narrative Visualizations (MSNV) can be challenging due to the need to integrate textual and visual information. This problem has prompted researchers to design dynamic guidance meant to ease the mapping of the information provided in the two modalities. We contribute to this line of work by evaluating gaze-driven adaptive guidance that dynamically links relevant sentences in the text to the corresponding datapoints in the visualizations (see Fig. 1). We conducted a user study that involved participants reading a series of MSNVs extracted from real-world sources. Results show that the adaptive links significantly improve the comprehension of the MSNVs as compared to receiving no guidance. This improvement comes at no expense of user reading time, and is consistent regardless of the MSNV complexity.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.044
GPT teacher head0.332
Teacher spread0.287 · 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

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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