Comparing a Scanning Ambiguous Keyboard to the On-screen QWERTY Keyboard
Bibliographic record
Abstract
This paper explores text entry on a scanning ambiguous keyboard (SAK) and the Windows on-screen keyboard (OSK) operating in scanning mode. The SPACEBAR was used for physical input with both keyboards. Testing involved 12 participants entering five phrases of text with each keyboard. On entry speed, the means were 5.06 wpm for the SAK and 2.67 wpm for the OSK, thus revealing a significant speed advantage for the SAK. However, the character-level error rate of 13.3% for the SAK was significantly higher than the error rate of 2.4% for the OSK. On subjective preference, 7 of 12 participants preferred the Windows OSK over the SAK, citing familiarity with the QWERTY layout as the most common reason. However, participants appreciated the efficiency of the SAK keyboard. A limitation of the results is the small amount of text entered.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".