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Record W2576563086 · doi:10.1017/s0142716416000485

The acquisition of tense morphology over time by English second language children with specific language impairment: Testing the cumulative effects hypothesis

2017· article· en· W2576563086 on OpenAlexafffund
Johanne Paradis, Ruiting Jia, Antti Arppe

Bibliographic record

VenueApplied Psycholinguistics · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSpecific language impairmentGrammaticalityPsychologyMorphemeDevelopmental psychologyLanguage developmentPast tenseSecond-language acquisitionLanguage acquisitionLinguisticsGrammarVerb

Abstract

fetched live from OpenAlex

ABSTRACT The cumulative effects hypothesis (CEH) claims that bilingual development would be a challenge for children with specific language impairment (SLI). To date, research on second language (L2) children with SLI has been limited mainly to their early years of L2 exposure; however, examining the long-term outcomes of L2 children with SLI is essential for testing the CEH. Accordingly, the present study examined production and grammaticality judgments of English tense morphology from matched groups of L2 children with SLI and L2 children with typical development (TD) for 3 years, from ages 8 to 10 with 4–6 years of exposure to English. This study found that the longitudinal acquisition profile of the L2 children with SLI and TD was similar to the acquisition profile reported for monolinguals with SLI and TD. Furthermore, L2-SLI children's accuracy with tense morphology was similar to that of their monolingual age peers with SLI at the end of the study, and exceeded that of younger monolingual peers with SLI whose age matched the L2 children's length of exposure to English. These findings are not consistent with the CEH, but instead show that morphological acquisition parallel to monolinguals with SLI is possible for L2 children with SLI.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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.

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

Citations18
Published2017
Admission routes2
Has abstractyes

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