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Record W2001399633 · doi:10.1017/s1366728909990241

Crosslinguistic transfer in the acquisition of compound words in Persian–English bilinguals

2009· article· en· W2001399633 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

VenueBilingualism Language and Cognition · 2009
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPersianLinguisticsNeuroscience of multilingualismPsychologyIndo-European languagesDominance (genetics)Language transferLanguage educationPhilosophyComprehension approachChemistry

Abstract

fetched live from OpenAlex

Crosslinguistic transfer in bilingual language acquisition has been widely reported in various linguistic domains (e.g., Döpke, 1998; Nicoladis, 1999; Paradis, 2001). In this study we examined structural overlap (Döpke, 2000; Müller and Hulk, 2001) and dominance (Yip and Matthews, 2000) as explanatory factors for crosslinguistic transfer in Persian–English bilingual children's production of novel compound words. Nineteen Persian monolinguals, sixteen Persian–English bilinguals, and seventeen English monolinguals participated in a novel compound production task. Our results showed crosslinguistic influence of Persian on English and of English on Persian. Bilingual children produced more right-headed compounds in Persian, compared with Persian monolinguals, and in their English task, they produced more left-headed compounds than English monolinguals. Furthermore, Persian-dominant bilinguals tended more towards left-headed compounds in Persian than the English-dominant group. These findings point to both structural overlap and language dominance as factors underlying crosslinguistic transfer.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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