Progressive multiples for communication-minded visualization
Bibliographic record
Abstract
This paper describes a communication-minded visualization called progressive multiples that supports both the forensic analysis and presentation of multidimensional event data. We combine ideas from progressive disclosure, which reveals data to the user on demand, and small multiples [21], which allows users to compare many images at once. Sets of events are visualized as timelines. Events are placed in temporal order on the x-axis, and a scalar dimension of the data is mapped to the y-axis. To support forensic analysis, users can pivot from an event in an existing timeline to create a new timeline of related events. The timelines serve as an exploration history, which has two benefits. First, this exploration history allows users to backtrack and explore multiple paths. Second, once a user has concluded an analysis, these timelines serve as the raw visual material for composing a story about the analysis. A narrative that conveys the analytical result can be created for a third party by copying and reordering timelines from the history. Our work is motivated by working with network security administrators and researchers in political communication. We describe a prototype that we are deploying with administrators and the results of a user study where we applied our technique to the visualization of a simulated epidemic.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".