Designing an Integrated Bookmark / History System for Web Browsing
Bibliographic record
Abstract
Current commercial web browsers such as Netscape Navigator and Microsoft Internet Explorer attempt to make it easier for users to return to previously visited web pages. They offer several important facilities for doing this, with the major ones being the Back button, the history list, and bookmarks. In theory, these mechanisms should be heavily used, for almost 60 % of all pages a person visits are to ones that they had seen previously [Tauscher and Greenberg 1997]. Yet research indicates several problems with these mechanisms. While Back is heavily used, people have an incorrect model of how it works, which leads to surprises when just-visited pages are no longer reachable [Cockburn and Jones 96; Greenberg and Cockburn 99]. Also, the bookmark and history systems are not used very frequently [Tauscher and Greenberg 97; Abrahms, Baeker, Chignell 98]. We believe that one of the reasons for these problems is that browsers provide revisitation systems in a fragmented, un-integrated manner. Back, history and bookmarks all use dissimilar underlying models, different interfaces, and various ways of sorting and presenting groups of candidate pages. In this research, our goal is to integrate the idea of Back, history and bookmarks into a single integrated revisitation system that captures the best features while remedying their known deficiencies. We are currently developing our prototype system (see Figure) that works within Microsoft Internet
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".