Readability Research: An Interdisciplinary Approach
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
The control provided by digital displays over how visual information ispresented to readers has the potential to improve reading for each and every reader, regardless of ability or diagnosis. On screens, text is fluid,allowing for individual customization based on reader needs, content, and reading task. This represents a profound shift in how we think about reading, because text is no longer rendered immutable by writers, designers or publishers at a single stage, and human-computer interaction research is key to realizing its potential. Targeted changes to the visual characteristics of text on screens increases the ease with which a reader can process and derive meaning. In this review, we provide a comprehensive introductionto interdisciplinary methodologies, tools, and materials required for readability research focused on the individual reader. We call on the HCI community to contribute to our growing understanding of readers' needs, to study the interactions between text, user, and task, and to build the tools and interfaces needed to improve reading outcomes for all.
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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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