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Record W1985601219 · doi:10.1017/s0305000910000218

Bilingual children's acquisition of the past tense: a usage-based approach

2010· article· en· W1985601219 on OpenAlexaff
Johanne Paradis, Elena Nicoladis, Martha Crago, Fred Genesee

Bibliographic record

VenueJournal of Child Language · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill UniversityDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsPast tensePsychologyLinguisticsVariation (astronomy)Neuroscience of multilingualismLanguage acquisitionPresent tenseTest (biology)VerbMathematics education

Abstract

fetched live from OpenAlex

Bilingual and monolingual children's (mean age=4;10) elicited production of the past tense in both English and French was examined in order to test predictions from Usage-Based theory regarding the sensitivity of children's acquisition rates to input factors such as variation in exposure time and the type/token frequency of morphosyntactic structures. Both bilingual and monolingual children were less accurate with irregular than regular past tense forms in both languages. Bilingual children, as a group, were less accurate than monolinguals with the English regular and irregular past tense, and with the French irregular past tense, but not with the French regular past tense. However, bilingual children were as accurate as monolinguals with the past tense in their language of greater exposure, except for English irregular verbs. It is argued that these results support the view that children's acquisition rates are sensitive to input factors, but with some qualifications.

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)0.004
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.248
Teacher spread0.243 · 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

Citations166
Published2010
Admission routes1
Has abstractyes

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