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Record W2118008713 · doi:10.1017/s0142716405050216

Comprehension and production of French object clitics by child second language learners and children with specific language impairment

2005· article· en· W2118008713 on OpenAlexafffund
Theres Grüter

Bibliographic record

VenueApplied Psycholinguistics · 2005
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill University
FundersMcGill University
KeywordsComprehensionPsychologyCliticSpecific language impairmentLinguisticsObject (grammar)Task (project management)SentenceCognitive psychology

Abstract

fetched live from OpenAlex

The objective of this research was to compare child second language (L2) learners and children with specific language impairment (SLI) on both production and comprehension in order to investigate whether the similarity of their error profiles observed in spontaneous production extends to comprehension. Results are presented from an elicited production and a sentence–picture matching task targeting accusative object clitics in French. As groups, both L2 learners and children with SLI show a low rate of clitic suppliance in production, yet perform well on the comprehension task. No statistically significant differences are found between the two groups on either task. Analyses of individual results, however, reveal diversity within both groups. Although there seems to be a correlation between performance in production and comprehension in the L2 group, this is not the case in the SLI group.

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)0.004
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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

Citations96
Published2005
Admission routes2
Has abstractyes

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