Disambiguation of imprecise input with one-dimensional rotational text entry
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.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".