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Record W3087656491 · doi:10.1177/0267658320956704

L2 transfer of L1 island-insensitivity: The case of Norwegian

2020· article· en· W3087656491 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSecond language Research · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNorwegianRestructuringDependency (UML)LinguisticsFiller (materials)PsychologyComputer scienceArtificial intelligenceEngineeringPolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

Norwegian allows filler-gap dependencies into embedded questions, which are islands for filler-gap dependency formation in English. We ask whether there is evidence that Norwegian learners of English transfer the functional structure that permits island violations from their first language (L1) to their second language (L2). In two acceptability judgment studies, we find that Norwegians are more likely to accept ‘island-violating’ filler-gap dependencies in L2 English if the corresponding filler-gap dependency is acceptable in Norwegian: Norwegian learners variably accept English sentences with dependencies into embedded questions, but not into subject phrases. These results are consistent with models that permit transfer of abstract functional structure. Norwegians are still less likely to accept filler-gap dependencies into English embedded questions than Norwegian embedded questions. We interpret the latter finding as evidence that, despite transfer, Norwegian speakers may partially restructure their L2 English analysis. We discuss how indirect positive evidence may play a role in helping learners restructure.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.070
GPT teacher head0.316
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