An Empirical Characterisation of Electronic Document Navigation
Bibliographic record
Abstract
To establish an empirical foundation for analysis and redesign of \ndocument navigation tools, we implemented a system that logs all \nuser actions within Microsoft Word and Adobe Reader. We then \nconducted a four month longitudinal study of fourteen users’ document \nnavigation activities. \nThe study found that approximately half of all documents manipulated \nare reopenings of previously used documents and that recent \ndocument lists are rarely used to return to a document. The two \nmost used navigation tools (by distance moved) are the mousewheel \nand scrollbar thumb, accounting for 44% and 29% of Word movement \nand 17% and 31% of Reader navigation. Participants were \ngrouped into stereotypical navigator categories based on the tools \nthey used the most. Majority of the navigation actions observed \nwere short, both in distance (less than one page) and in time (less \nthan one second). We identified three types of within document \nhunting, with the scrollbar identified as the greatest contributor.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".