Text Entry via Character Stroke Disambiguation for an Adolescent With Severe Motor Impairment and Cortical Visual Impairment
Bibliographic record
Abstract
This study proposed a single-switch text entry system by hierarchical scanning of character strokes for an 11-year-old girl with severe physical disabilities and low vision. She could only perceive magnified straight line segments and chords presented against high-contrast, colored backgrounds. In a descriptive case study, the participant used the proposed system in the community for 8 months. Assessment included theoretical evaluation of text entry performance and empirical evaluation of the participant's proficiency. The proposed system had a lower error-free text entry rate but comparable proneness to user error as a real-world implementation of row-column virtual scanning keyboard with character frequency layout. The participant's proficiency, in terms of mean number of single-switch activations and time to type one character, showed statistically significant improvements as the case study progressed. The proposed system feasibly addressed the participant's typing needs, in a context where traditional row-column scanning and codeword-based text entry systems were not successful.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".