THE UNIVERSITY OF CALGARY Integrating Back, History and Bookmarks in Web Browsers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While there has been much discourse on web site usability, there has been comparatively little done about the usability problems that plague conventional web browser software that people use to browse the web. This is troubling, as gains that can be made on the browser software will improve the usability of all web sites. Most web browsers include Back, History and Bookmark facilities that simplify how people return to previously seen pages. While useful in theory, studies have found that users do not take enough advantage of these facilities. This thesis examines the usability issues present in these revisitation facilities. These issues include the disparate models that the user must understand to operate them and the mismatch between how they represent pages and how users remember their pages. To explore this issue, we ran an experiment where we compared how well people could recognize previously visited pages when shown its title, URL address or thumbnail image. The results of this experiment were translated into the design and implementation of our own alternative revisitation system. It is based on the single model of a recency-ordered history list to integrate Back, History and Bookmarks. Enhancements include: Back as a
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it