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Record W2062850627 · doi:10.1044/1092-4388(2006/070)

Nonword Repetition: A Comparison of Tests

2006· article· en· W2062850627 on OpenAlexaff
Lisa M. D. Archibald, Susan E. Gathercole

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2006
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsRepetition (rhetorical device)AudiologySpecific language impairmentPsychologyConsonantCognitionShort-term memoryDevelopmental psychologyCognitive psychologyWorking memoryLinguisticsMedicineVowel

Abstract

fetched live from OpenAlex

PURPOSE: This study compared performance of children on 2 tests of nonword repetition to investigate the factors that may contribute to the well-documented nonword repetition deficit in specific language impairment (SLI). METHOD: Twelve children with SLI age 7 to 11 years, 12 age-matched control children, and 12 control children matched for language ability completed 2 tests of nonword repetition: the Children's Test of Nonword Repetition (CNRep) and the Nonword Repetition Test (NRT). RESULTS: The children with SLI performed significantly more poorly on both tests than typically developing children of the same age. The SLI group was impaired on the CNRep but not the NRT relative to younger children with similar language abilities when adjustments were made for differences in general cognitive ability. The children with SLI repeated the lengthiest nonwords and the nonwords containing consonant clusters significantly less accurately than the control groups. CONCLUSION: The evidence suggests that the nonword repetition deficit in SLI may arise from a number of factors, including verbal short-term memory, lexical knowledge, and output processes.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.079
GPT teacher head0.455
Teacher spread0.376 · 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

Citations199
Published2006
Admission routes1
Has abstractyes

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