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Record W1992851280 · doi:10.1017/s0142716407070270

The weaker language in early child bilingualism: Acquiring a first language as a second language?

2007· article· en· W1992851280 on OpenAlexaff
Jürgen M. Meisel

Bibliographic record

VenueApplied Psycholinguistics · 2007
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Calgary
FundersDeutsche Forschungsgemeinschaft
KeywordsNeuroscience of multilingualismPsychologyGermanDevelopmental linguisticsLinguisticsSecond-language acquisitionFirst languageLanguage acquisitionCompetence (human resources)Linguistic competenceLanguage transferSecond-language attritionComprehension approachCognitive psychologyLanguage educationSocial psychologyMathematics education

Abstract

fetched live from OpenAlex

Past research demonstrates that first language (L1)-like competence in each language can be attained in simultaneous acquisition of bilingualism by mere exposure to the target languages. The question is whether this is also true for the “weaker” language (WL). The WL hypothesis claims that the WL differs fundamentally from monolingual L1 and balanced bilingual L1 and resembles second language (L2) acquisition. In this article, these claims are put to a test by analyzing “unusual” constructions in WLs, possibly indicating acquisition failure, and by reporting on analyses of the use of French by bilinguals whose dominant language is German. The available evidence does not justify the claim that WLs resemble L2. Instead, it shows that WL development can be delayed, but does not suggest acquisition failure. Finally, reduced input is unlikely to cause acquisition failure. The fundamental issue at stake is to explore the limits of the human language making capacity.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.303
Teacher spread0.295 · 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

Citations185
Published2007
Admission routes1
Has abstractyes

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