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Record W2160559232 · doi:10.1080/01449290110049790

The role of visual search in the design of effective soft keyboards

2001· article· en· W2160559232 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehaviour and Information Technology · 2001
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsSaint John Regional HospitalUniversity of New Brunswick
Fundersnot available
KeywordsText entryKey (lock)Human–computer interactionComputer scienceMatching (statistics)Component (thermodynamics)Mobile deviceVisual searchEngineering drawingArtificial intelligenceWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

As portable, handheld computing devices become more common, alternatives to traditional keyboards must be explored. These alternatives must be compact, lightweight and sufficiently efficient to support the users' tasks. One alternative is the use of small physical keyboards or soft keyboards presented on touch-sensitive surfaces. Many alternative layouts have been explored, including the QWERTY, Dvorak, telephone and various alphabetic organizations. Soukoreff and MacKenzie proposed a model to predict typing times for alternative layouts, but have experienced limited success matching their predictions to observed performance. This paper proposes a revision of the visual search component of their model that considers the familiarity of the organization and the number of letters represented by each key. Results are reported of an experiment that supports the claim that both familiarity and the number of letters per key must be considered when predicting visual search times for alternative keyboard layouts.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.110

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.285
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it