Disambiguation of imprecise input with one-dimensional rotational text entry
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
We introduce a distinction between disambiguation supporting continuous versus discrete ambiguous text entry. With continuous ambiguous text entry methods, letter selections are treated as ambiguous due to expected imprecision rather than due to discretized letter groupings. We investigate the simple case of a one-dimensional character layout to demonstrate the potential of techniques designed for imprecise entry. Our rotation-based sight-free technique, Rotext, maps device orientation to a layout optimized for disambiguation, motor efficiency, and learnability. We also present an audio feedback system for efficient selection of disambiguated word candidates and explore the role that time spent acknowledging word-level feedback plays in text entry performance. Through a user study, we show that despite missing on average by 2.46--2.92 character positions, with the aid of a maximum a posteriori (MAP) disambiguation algorithm, users can average a sight-free entry speed of 12.6wpm with 98.9% accuracy within 13 sessions (4.3 hours). In a second study, expert users are found to reach 21wpm with 99.6% accuracy after session 20 (6.7 hours) and continue to grow in performance, with individual phrases entered at up to 37wpm. A final study revisits the learnability of the optimized layout. Our modeling of ultimate performance indicates maximum overall sight-free entry speeds of 29.0wpm with audio feedback, or 40.7wpm if an expert user could operate without relying on audio feedback.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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