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Record W2071545643 · doi:10.1207/s15326942dn2102_3

Speech Production Errors in Adults With and Without Down Syndrome Following Verbal, Written, and Pictorial Cues

2002· article· en· W2071545643 on OpenAlexafffund
Lindsay Bunn, Dominic A. Simon, Timothy N. Welsh, Chris Watson, Digby Elliott

Bibliographic record

VenueDevelopmental Neuropsychology · 2002
Typearticle
Languageen
FieldNeuroscience
TopicWilliams Syndrome Research
Canadian institutionsMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthNational Institute of Child Health and Human DevelopmentCanada Research ChairsNational Down Syndrome Society
KeywordsPsychologySpeech productionDown syndromeAudiologyStimulus (psychology)Short-term memoryDevelopmental psychologyCognitionCognitive psychologyWorking memoryLinguisticsNeuroscience

Abstract

fetched live from OpenAlex

Adults with Down syndrome and adults with developmental delays not related to Down syndrome were asked to read, repeat, and formulate speech from a picture following the presentation of 2- and 4-word and picture sequences. The speech sequences were produced either immediately after stimulus presentation or following a 5-sec response delay. Overall, participants with Down syndrome produced more memory errors than persons without Down syndrome. Participants with Down syndrome also committed more speech production errors than the other participants, but only when they were required to repeat what they had heard, or to formulate speech from pictures. The speech production performance of the two groups was equivalent in the read condition. These results are discussed with reference to Elliott, Weeks, and Elliott's (1987) model of cerebral specialization, and to verbal short-term memory in persons with Down syndrome.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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

Citations16
Published2002
Admission routes2
Has abstractyes

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