Bibliographic record
Abstract
Active Reading is an important part of a knowledge worker’s activities; it involves highlighting, writing notes, marking with symbols, etc., on a document. Many Active Reading applications have been designed in seeking to replicate the affordances of paper through digital-ink-based annotation tools. However, these applications require users to perform numerous steps to use various types of annotation tools, which impose an unnecessary cognitive load, distracting them from their reading tasks. In this thesis, we introduce GestAnnot, an Active Reading application for tablet computers that takes a fundamentally different approach of incorporating multi-touch gesture techniques for creating and manipulating annotations on an e-document, thus offering a flexible and easy- to-use annotation solution. Based on the literature review, we designed and developed GestAnnot and then performed lab and field evaluations of the software. In lab evaluation, GestAnnot performed better than one of the best existing annotation application in many aspects, including number of steps. The design was then refined based on the feedback received. The field evaluation of the improved design helped us to understand the performance of the application in the real world. We proposed a set of design guidelines through the feedback received from both evaluations, which any future Active Reading application could benefit from.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".