Multiple Workspaces in Visual Analytics
Bibliographic record
Abstract
Exploratory visual analysis is an iterative process, where analysts often start from an overview of the data. Subsequently, they often pursue different hypotheses through multiple rounds of interaction and analysis. Commercial visualization packages support mostly a model with a single analysis path, where the system view represents only the final state of the users' current analysis. In this paper, we investigate the benefit of using multiple workspaces to support alternative analyses, enabling users to create different workspaces to pursue multiple analysis paths at the same time. We implemented a prototype for multiple workspaces using a multi-tab design in a visual analytics system. The results of our user studies show that multiple workspaces: enable analysts to work on concurrent tasks, work well for organizing an analysis, and make it easy to revisit previous parts of their work.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".