Exploiting history to reduce interaction costs in collaborative analysis
Bibliographic record
Abstract
When analysts work in a distributed fashion, they need to understand what their collaborators have done and what avenues of analysis remain uninvestigated. Although visualization history has the potential to communicate such information, the common representations are often limited to sequential lists of past work. Such representations do not make it easy to understand the analytic coverage of the dimension space (i.e. which dimensions have been investigated and which have not). This makes it difficult for an analyst to plan their next steps, particularly when the number of dimensions is large. In this paper, we propose representing the prior analysis from a dimension coverage perspective. Dimension view provides a unique perspective that can facilitate exploratory analysis by enabling analysts to easily identify what dimensions have been examined and in what combinations. We hypothesize that addition of this view to common representations of visualization history will reduce cognitive and interaction costs by helping the analyst to discover data subsets to explore. We studied the effects of this view on a distributed collaborative visualization process. Our findings show that providing views of the dimension and data space reduces time required for identifying and investigating unexplored regions and increases the accuracy of this understanding. In addition, providing these views results in a larger coverage of entire dimension space.
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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 | 0.021 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".