An accessible, large-print, listening and talking e-book to support families reading together
Bibliographic record
Abstract
Reading is an activity that is not only informative or pleasurable, but can have significant social benefits. Especially in a family setting, it is part of the interaction between children and their parents, it helps create a bond between children and their grandparents, and even bring adults and their older parents closer. However, with families increasingly living or spending time in different locations or managing busy schedules that afford very little time together, the social opportunities enabled by reading are often lost. Furthermore, reading can be a challenge for older adults or for those with impaired eyesight. To address these problems, we are proposing ALLT -- an Accessible, Large-Print, Listening and Talking e-book. ALLT is a tablet-based e-reading application that enhances the capabilities of e-book readers through customizable and intelligent accessibility features. It provides support for asynchronous "reading together" by synchronizing the audio recording of one user with the text that is later read by another user. This addresses the needs of a variety of users, from visually impaired adults reading together with a loved one, to children being able to replay an interactive story previously read together with their grandparents. In this demo paper we present ALLT's features and detail how they support asynchronously reading together.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".