MétaCan
Menu
Back to cohort
Record W2798346558 · doi:10.14236/ewic/hci2017.103

Comparing a Scanning Ambiguous Keyboard to the On-screen QWERTY Keyboard

2017· article· en· W2798346558 on OpenAlexaff
Chris T. Waddington, I. Scott MacKenzie, Janet C. Read, Matthew Horton

Bibliographic record

VenueElectronic workshops in computing · 2017
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsYork University
Fundersnot available
KeywordsText entryComputer scienceMode (computer interface)Word error rateCharacter (mathematics)Human–computer interactionComputer graphics (images)Speech recognitionMathematics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.025
GPT teacher head0.297
Teacher spread0.272 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueElectronic workshops in computingSame topicInteractive and Immersive DisplaysFrench-language works237,207