A Design Space for Visualization Onboarding in Data-Driven Stories
Bibliographic record
Abstract
Data-Driven Stories (DDS) are stories that combine text and data portrayed as visualization in a narrative format. They are among the popular ways of communicating information by online medias nowadays. For DDS authors and designers, it's important to minimize the risk of misinterpreting visualizations by their readers. Visualization onboarding, embedding knowledge and guidance have been meant to provide adequate support for readers to understand visualizations as they progress through DDS. Onboarding is a continuous mechanism which involves various DDS elements and interactions on each step. Several previous studies attempted to identify and classify storytelling techniques in DDS. While these techniques prospect a satisfactory communication, it's not clear how they can be applied to facilitate the visualization understanding throughout the story. They rather conceptualized different aspects of storytelling individually, and as such, the chronology of onboarding steps has been missed. Although their techniques and design spaces represent a tangible level of abstraction, they will not benefit authors in the story design process. Authors either rely on their guess work or mimic previous DDS to accommodate support in their DDS scenarios. In this project, our overall goal is to propose a multidimensional design space for onboarding techniques in DDS that can benefit to authors during their design process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".