Will it be a capital letter: signalling case mode in mobile phones
Bibliographic record
Abstract
While there are well established guidelines for interaction via mouse and keyboard, new forms of interaction being devised for small handheld devices have yet to be standardised. There is a case for re-visiting basic principles for user interface design such as how to signal mode. Two ways of signalling case mode when editing text into a small handheld device such as a mobile phone are considered in this paper. One is through the system prompt, e.g. ‘Entry:’, the other is through the case of the last letter displayed in response to a button push. Two unsupervised web-based experiments are described which show that users are sensitive to both these signals for case mode. The first experiment manipulated the prompt in a text entry task using a web simulation of a novel mobile device. The results showed that users’ expectations were influenced by the case of the letters in the prompt. Users took many more trials to learn to expect a case inconsistent with the model provided by the prompt. The second experiment manipulated both the case of the letters in the prompt and the case of the last letter displayed. The results replicated the findings above and demonstrated a strong effect of the case of the last letter displayed. Guidelines for signalling case mode and a notation (Interaction Units) are suggested that might be used to reason about low level interaction design with handheld devices.
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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 | 0.006 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".