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Record W3000198490 · doi:10.1075/lab.18086.sch

Object clitic production in French-speaking L2 children and children with SLI

2020· article· en· W3000198490 on OpenAlexaff
Maureen Scheidnes, Laurice Tuller, Philippe Prévost

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMemorial University of Newfoundland
FundersAgence Nationale de la Recherche
KeywordsCliticObject (grammar)LinguisticsPsychologyProduction (economics)Computer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Abstract This study examines French object clitic production in 20 typically developing L2 children (L1 English) compared to 19 monolingual children with SLI. We collected spontaneous and elicitation data twice at a one-year interval (T1, T2) in order to better evaluate the impact of age- and time-related factors on the L2-SLI comparison, as well as the impact of proficiency and working memory on object clitic production. The data revealed considerable group overlap at T1 in both tasks, but the L2 children produced significantly more object clitics than the SLI group at T2 in spontaneous language, thus suggesting that the L2-SLI overlap decreases when the L2 children have more language exposure. In elicitation, clitic production in both groups increased from T1 to T2 when all clitic types were included, but when only 3p accusative clitics were analyzed, the L2 children outperformed the SLI group. Age played an important role in elicitation in both groups, thus suggesting that mature performance systems, including unimpaired working memory, are required for object clitic production in this task. Language-related measures were linked to both tasks, thus suggesting that object clitic production is particularly sensitive to overall language proficiency, possibly because of issues with resource allocation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.267
Teacher spread0.208 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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