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Record W2738773807

Story Retell Narratives in Five School-Aged Children with Language Impairment

2016· article· en· W2738773807 on OpenAlexaboutno aff
Megan Bradshaw Deere

Bibliographic record

VenueScholarsArchive (Brigham Young University) · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeLanguage impairmentSpecific language impairmentPsychologyLinguisticsDevelopmental psychologyMedicinePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Many children identified with Language Impairment (LI) demonstrate difficulty comprehending and producing narratives. Their narratives are often structurally less complex and of overall poorer quality than those produced by their typically developing peers. These difficulties may negatively impact the academic and social success of children with LI. This thesis evaluates the performance of five school-aged children with LI on a story retell probe embedded within an intervention designed to address their social and emotional language abilities. During the 10-week intervention, participants completed a series of story retell probes using wordless picture books. The story stimuli were taken from the Edmonton Narrative Norms Instrument, which included six stories (divided into two story sets), elicited twice (12 total story retells). The production of story grammar (SG) categories was analyzed for each story retell. The results for each participant and SG category varied greatly, but all participants had difficulty producing the more complex SG elements. Although each participant demonstrated some improvement from the first retell to the second on at least one story, overall performance remained fairly stable over the 10-week period. Future research is needed to determine effective ways to support more complex story narratives in children with LI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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