Bibliographic record
Abstract
<section class="page-section"><div id="i50" class="featureboxedText"><div class="head"><span>VisualEyes</span></div><div id="i51" class="aside"><div id="i52" class="figure floatinline"><img src="/images/virtual/visualeyes/10.4135_9781529776904-img1.jpg" alt="" class="graphic"></div></div> <span class="hi-bold">Price:</span> Free <span class="hi-bold">Open Source?:</span> Closed Source <span class="hi-bold">Platform(s):</span> Web </div></section><section class="page-section"><div id="i54" class="figure floatinline"><img src="/images/virtual/visualeyes/10.4135_9781529776904-fig1.jpg" alt="A screenshot shows a zoomed-in satellite view of a coastal region. A timeline under the view shows the major events between 1749 and 1821, during the Colonial era, Revolutionary era, New Nation era, and Expansion era." class="graphic"></div></section> 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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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; both teacher heads agree on what is shown here.
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".