MétaCan
Menu
Back to cohort
Record W2343599480

Entering handwriting into computers using a digital pen

2007· article· en· W2343599480 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

VenueAnnual Conference on Computers · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsHandwritingComputer scienceHandwriting recognitionNatural language processingSoftwareAffect (linguistics)Artificial intelligenceLinguisticsSpeech recognitionProgramming languageFeature extraction
DOInot available

Abstract

fetched live from OpenAlex

Transposing handwriting through a digital pen into typed text for language learning are discussed. Researchers agree that there are specificities of first language (L1) writing that could affect writing in a second language (L2) because writing styles are language specific. In our study of the conversion of handwritten notes to computer printed text with the Logitech [1] MyScripts® handwriting recognition software in both French and English, we uncovered what affected the conversion to typed texts of the writing modes in L1 and L2 by the same writer. The question of penmanship is explored as is the question of transfer from handwritten language to the printed conversion. Suggestions are made about possible adjustments, both to the technology and for the user in the passage from L1 to L2.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.051
GPT teacher head0.352
Teacher spread0.301 · 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