The History of ViSta: The Visual Statistics System
Bibliographic record
Abstract
Abstract ViSta is a project that focuses on dynamic and interactive graphics for statistics and was initiated by the late Forrest W. Young at the beginning of the 1990s. For over approximately 20 years, Forrest and other collaborators, including the authors of this article, have used ViSta for experimenting with these kinds of graphics in different settings, applying them to different scenarios of data and statistical analysis, searching to develop the right combination of features most appropriate in each case. In this time, ViSta evolved quite considerably, going through what we reckon were three different stages, namely: the initial one setting forth the foundations of ViSta; the second period where versions 5 and 6 of ViSta were released; and the consolidation period when a book summarizing the lessons learnt in the project was published. This book was titled ‘Visual Statistics: Seeing your data with interactive and dynamic graphics’ and was completed in the last days of life of Forrest, who continued to work enthusiastically in the project even though his health was seriously deteriorating during that time. This article is a tribute to this work, but also describes the innovative features of ViSta, many of which are still relevant today. WIREs Comput Stat 2012, 4:295–306. doi: 10.1002/wics.1203 This article is categorized under: Software for Computational Statistics > Software/Statistical Software
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".