Bibliographic record
Abstract
VisualEyes Price: Free Open Source?: Closed Source Platform(s): Web VisualEyes is a web-based (HTML5) authoring tool for data visualization in humanities subjects. It is part of the Shanti Interactive suite of tools developed by the University of Virginia. It is designed to weave historical primary sources into images, maps, charts, videos, and data, creating interactive and dynamic visualizations. Some example projects using VisualEyes include exploring cultural changes in Tibet and mapping encounters in early Canada, all featured on the website. No programming knowledge is needed to get started with VisualEyes. There is an extensive tutorial on Google Docs that goes through the steps and resources required to start creating with VisualEyes. There are three core elements of the VisualEyes window, each dealing with different aspects of the current project: Map, Timeline, and Story. The window is essentially filled with information from a Google Docs spreadsheet of your project. The central feature of most VisualEyes projects are timeline and map events. Since project information is stored on ...
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.719 | 0.692 |
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".