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Record W4387707604 · doi:10.1075/lab.22058.bus

Verb placement in L3 French and L3 German

2023· article· en· W4387707604 on OpenAlexaff
Guro Busterud, Anne Dahl, Dave Kush, Kjersti Faldet Listhaug

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVerbLinguisticsGermanSubject (documents)Reflexive verbGenerative grammarComputer scienceModal verbPsychologyRepresentation (politics)Natural language processingArtificial intelligencePhilosophyPolitical science

Abstract

fetched live from OpenAlex

Abstract This article explores cross-linguistic influence and the relationship between surface structure and underlying syntactic structure in L3 acquisition of verb placement in L1 Norwegian L2 English learners of L3 German or French, respectively. In these languages, verb placement varies systematically. Previous research has found transfer from both L1 and L2 in similar language combinations. Using an acceptability judgment task, we tested verb placement in non-subject-initial and subject-initial sentences. Findings indicate that L3 French learners performed better on non-subject-initial sentences compared to subject-initial sentences, whereas the opposite was the case in L3 German. We argue that our findings can be explained by a generative account of verb movement and are compatible with an analysis where verbs do not move, or do not move far enough, in the L3 learners’ underlying syntactic representation. Following the assumption that verb movement is a costly operation, we argue that the syntactic operation verb movement is constrained by principles of economy in L3 acquisition, and that economy plays a role in determining cross-linguistic influence in multilingual acquisition. Our account is compatible with a uniform analysis of the acquisition of verb movement in L1, L2 and L3, and underlines the qualitative similarities in different acquisition processes.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.168
GPT teacher head0.321
Teacher spread0.153 · 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 designBench or experimental
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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

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