Visualization of Web Spaces: State of the Art and Future Directions
Bibliographic record
Abstract
The World Wide Web is a dominant global communication medium and knowledge repository. It is used by a great number of people with a variety of computer skills hence its usability is critical. As with many large information collections, the challenge with web usability is understanding the structure of a collection of information objects (web pages) to find relevant ones for satisfying a specific information need. Web sites are organized in a hyperlinked structure that somewhat addresses this challenge. However, this “connectedness” also causes the now well-known “lost in cyberspace” phenomenon where one may get confused within the complex organization of a web site. Meanwhile, information exploration on the web is not limited to browsing a web site. The problem of finding relevant information applies to a collection of pages that come from various web sites as in the case of the results of a “less than perfectly constructed” search query. Information visualization has been proposed as a way to cope with these problems by taking advantage of people’s innate perceptual skills to support their cognitive skills. Many paradigms have been proposed for the visual presentation of web spaces (i.e. structured or unstructured collection of web pages). This study surveys these paradigms to provide a map of where the research in this field is, and what directions future research and practice can take. For this, we introduce a classification scheme to help in the systematic understanding of web visualization and for providing a framework for the development of future visualizations.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".