Exploiting history to reduce interaction costs in collaborative analysis
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it