MétaCan
Menu
Back to cohort

Specific Language Impairments in Children

2004· article· en· W2137979702 on OpenAlexaff
Marc F. Joanisse

Bibliographic record

VenueCurrent Directions in Psychological Science · 2004
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsSpecific language impairmentPsychologyGrammarPast tenseCognitive psychologyReading (process)PerceptionLinguisticsParallelsPhonologySentence processingSyntaxConnectionismCognitionVerbNeuroscience

Abstract

fetched live from OpenAlex

Theories of specific language impairment (SLI) in children turn on whether this deficit stems from a grammar-specific impairment or a more general speech-processing deficit. This issue parallels a more general question in cognitive neuroscience concerning the brain bases of linguistic rules. This more general debate frequently focuses on past-tense verbs, specifically, whether regular verbs (bake-baked) are encoded as rules, and whether irregular forms (take-took) are processed differently. Children with SLI have difficulties with past tenses, so SLI could represent an impairment to rules. An alternative theory explains past-tense deficits in SLI as resulting from a phonological deficit. Evidence for this theory has been obtained from connectionist models of past-tense impairments and from behavioral studies of language- and reading-impaired children. The data suggest that SLI is not an impairment to linguistic rules, that past-tense impairments can be explained as resulting from a perceptual deficit, and that a single processing mechanism is ideally suited to account for these children's difficulties.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.401
Teacher spread0.375 · 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

Citations108
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Directions in Psychological ScienceSame topicLanguage Development and DisordersFrench-language works237,207