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Record W1981181341 · doi:10.3109/02699206.2012.746397

Early-stage chunking of finger tapping sequences by persons who stutter and fluent speakers

2012· article· en· W1981181341 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClinical Linguistics & Phonetics · 2012
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsUniversity of Toronto
FundersToronto Rehabilitation Institute
KeywordsChunking (psychology)Finger tappingPsychologyTappingAudiologySequence (biology)Speech recognitionLinguisticsCognitive psychologyComputer scienceBiologyGenetics

Abstract

fetched live from OpenAlex

This research note explored the hypothesis that chunking differences underlie the slow finger-tap sequencing performance reported in the literature for persons who stutter (PWS) relative to fluent speakers (PNS). Early-stage chunking was defined as an immediate and spontaneous tendency to organize a long sequence into pauses, for motor planning, and chunks of fluent motor performance. A previously published study in which 12 PWS and 12 matched PNS practised a 10-item finger tapping sequence 30 times was examined. Both groups significantly decreased the duration of between-chunk intervals (BCIs) and within-chunk intervals (WCIs) over practice. PNS had significantly shorter WCIs relative to PWS, but minimal differences between groups were found for the number of, or duration of, BCI. Results imply that sequencing differences found between PNS and PWS may be due to differences in automatizing movements within chunks or retrieving chunks from memory rather than chunking per se.

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)0.003
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 score0.140
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.078
GPT teacher head0.419
Teacher spread0.341 · 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