Designing an Eyes-Reduced Document Skimming App for Situational Impairments
Bibliographic record
Abstract
Listening to text using read-aloud applications is a popular way for people to consume content when their visual attention is situationally impaired (e.g., commuting, walking, tired eyes). However, due to the linear nature of audio, such apps do not support skimming---a non-linear, rapid form of reading---essential for quickly grasping the gist and organization of difficult texts, like academic or professional documents. To support auditory skimming for situational impairments, we (1) identified the user needs and challenges in auditory skimming through a formative study (N=20), (2) derived the concept of "eyes-reduced" skimming that blends auditory and visual modes of reading, inspired by how participants mixed visual and non-visual interactions, (3) generated a set of design guidelines for eyes-reduced skimming, and (4) designed and evaluated a novel audio skimming app that embodies the guidelines. Our in-situ preliminary observation study (N=6) suggested that participants were positive about our design and were able to auditorily skim documents. We discuss design implications for eyes-reduced reading, read-aloud apps, and text-to-speech engines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".