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Record W2130346537 · doi:10.1352/1934-9556-50.4.332

A Longitudinal Study of Narrative Development in Children and Adolescents with Down Syndrome

2012· article· en· W2130346537 on OpenAlexaff
Patricia L. Cleave, Elizabeth Kay‐Raining Bird, Rachael Czutrin, Lindsey R. Smith

Bibliographic record

VenueIntellectual and developmental disabilities · 2012
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNarrativePsychologyDevelopmental psychologyComprehensionLanguage developmentNarrative inquiryPeriod (music)Linguistics

Abstract

fetched live from OpenAlex

The present study examined narrative development in children and adolescents with Down syndrome longitudinally. Narratives were collected from 32 children and adolescents with Down syndrome three times over a 1-year period. Both micro- and macrolevel analyses were conducted. Significant growth over the 1-year period was seen in semantic complexity and narrative structure. However, there was no evidence of growth in syntactic complexity or narrative length. Mental age and comprehension skills at Time 1 predicted scores in all 4 areas at Time 3. Expressive language skills added further to the prediction of syntactic complexity and story length.

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)0.006
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.294
Teacher spread0.256 · 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

Citations25
Published2012
Admission routes1
Has abstractyes

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