Two-level artificial-landmark scrollbars to improve revisitation in long documents
Bibliographic record
Abstract
Navigating to previously-visited pages is a trivial yet fundamental task in linear control-based document viewers. These widgets e.g., scrollbars often do not work well particularly for long documents. Existing solutions try to tackle this issue with bookmarks, search, history, and read wear but limited in terms of effort, clutter, and interpretability. To improve the revisitation support in long documents, we investigated the use of artificial landmarks similar to the visual augmentations available in physical books: coloring on page edges or indents cut into pages. We developed several artificial-landmark visualizations to represent page-locations in the scrollbar for many hundreds of pages long documents, and tested them in studies where participants visited multiple locations in long documents. Results indicate that using two columns of landmark icons significantly improved revisitation performance and preferred by users. Our two-level artificial-landmark augmented scrollbars can be a new way to support spatial memory development of long documents - and can be used either in isolation or in congregation with current techniques.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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".