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Record W2923125644 · doi:10.1177/0008417419832327

The effect of task complexity on handwriting kinetics

2019· article· en· W2923125644 on OpenAlexvenueno aff
Yu‐Chen Lin, Yen-Li Chao, Chieh‐Hsiang Hsu, Hsiao-Man Hsu, Po-Tsun Chen

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsHandwritingTask (project management)PsychologyCognitive psychologyComputer scienceArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND.: Knowledge regarding the relationship between writing kinetics and the difference among writing tasks is limited. PURPOSE.: This study examined the differences in handwriting performance when doing tasks with different levels of challenge from both temporal and kinetic perspectives among children in four different age groups. METHOD.: The cross-sectional design introduced a force-acquisition pen to detect differences of pen grip and writing kinetics among 170 school-age children doing writing tasks at different difficulty levels. Data were obtained on the force information of the digits and pen tip and the kinetic parameters to examine the coordination-and-control mechanism between the digits and pen. Statistical analyzes were carried out to indicate the differences in writing performance among groups and tasks. FINDINGS.: Statistical differences in the pen-grip forces, force fluctuation, and force ratio between grip and pen-tip forces were found when performing different writing tasks and among different age groups. IMPLICATIONS.: The study provides an alternative method to explore how writing performance among school-age children can vary according to the difficulty of the writing tasks.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.436

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.103
GPT teacher head0.395
Teacher spread0.292 · 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 teacher head, 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

Citations10
Published2019
Admission routes1
Has abstractyes

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