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Record W2091835662 · doi:10.1017/s0305000902005366

What's the difference between ‘toilet paper’ and ‘paper toilet’? French-English bilingual children's crosslinguistic transfer in compound nouns

2002· article· en· W2091835662 on OpenAlexaff
Elena Nicoladis

Bibliographic record

VenueJournal of Child Language · 2002
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyLinguisticsNounComprehensionAmbiguityNeuroscience of multilingualismLanguage acquisitionLanguage transferTransfer of trainingCognitive psychologyComprehension approachLanguage educationMathematics education

Abstract

fetched live from OpenAlex

Bilingual acquisition can shed light on the cues children used in acquiring language. The purpose of this paper was to examine whether frequency, ambiguity or language dominance could explain crosslinguistic transfer in compound nouns. Crosslinguistic transfer would appear in the form of compound reversals. 25 monolingual English children between the ages of three and four years and 25 age-matched French-English bilingual children were asked to create and indicate their understanding of novel compound nouns. In production, the bilingual children reversed compounds in English more often than the monolingual children but equally often in French and English. In comprehension, there were no differences between groups. These results cannot be explained by any previous explanation of transfer. Implications for the theory of language acquisition are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations139
Published2002
Admission routes1
Has abstractyes

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