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Record W2631563934

L1 Aquisition of Direct Object Clitic Doubling

2017· article· en· W2631563934 on OpenAlexaff
Mona Luiza Ungureanu

Bibliographic record

VenueLinguistica Atlantica · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsSecond-language acquisitionLanguage acquisitionCliticComputer scienceObject (grammar)Value (mathematics)PsychologyLinguisticsArtificial intelligenceMachine learning
DOInot available

Abstract

fetched live from OpenAlex

This paper discusses the first language acquisition of the Direct Object Clitic Doubling (henceforth [DOCD]) parameter in Romanian, a [+DOCD] language, and reports the results of a pilot experiment that identifies the default setting of this parameter. The hypothesis tested here assumes, following Sportiche (1996, 1997), that [-DOCD] is the default value of this parameter, while the [+DOCD] value is acquired on the basis of positive evidence. Following the Full Competence Hypothesis (FCH) as proposed by Poeppel & Wexler (1993) among others, I assume that functional categories (i.e. clitics) are present in the child’s grammar from the beginning. The working hypothesis in this paper predicts that in earlier stages of language acquisition children will entertain the [-DOCD] parameter, even when they are learning a [+DOCD] language. These predictions are borne out by the results of my study. This pilot experiment was designed to investigate the process of acquisition of the [+DOCD] value; in this respect, children at different stages of acquisition and adult control participants were tested. Two tasks focusing on the DOCD values were used: an elicited production task and an imitation task. We conclude that in the process of L1 acquisition of [DOCD] the default value is [-DOCD]. Key words: Romanian L1 acquisition, direct object clitics acquisition, clitic doubling acquisition, parameteric settings, functional categories.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.039
GPT teacher head0.277
Teacher spread0.238 · 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 designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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