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Record W3165787492 · doi:10.1515/zfs-2021-2025

Diversity and divergence in bilingual acquisition

2021· article· en· W3165787492 on OpenAlexaff
Jürgen M. Meisel

Bibliographic record

VenueZeitschrift für Sprachwissenschaft · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNeuroscience of multilingualismLinguisticsDivergence (linguistics)PsychologyDevelopmental linguisticsSecond-language acquisitionRule-based machine translationAffect (linguistics)Theoretical linguisticsLanguage acquisitionLinguistic universalComprehension approachNatural languagePhilosophy

Abstract

fetched live from OpenAlex

Abstract Bilingual settings are perceived as exemplary cases of linguistic diversity, and they are assumed to trigger cross-linguistic interaction. The rationale underlying this assumption is the belief that when more than one language is processed in a brain, this will inevitably affect the way in which linguistic knowledge is acquired, stored and used. However, this idea stands in conflict with results obtained by research on children acquiring two (or more) languages simultaneously. They have been demonstrated to be able to differentiate languages from early on and to develop competences qualitatively identical to those of monolinguals. These studies thus provide little evidence supporting the idea that bilingualism must lead to divergent grammatical development. The question then is what triggers alterations of bilinguals’ grammars, especially of the syntactic core, possibly resulting in non-native competences. This has been claimed to occur in the acquisition of second languages, weaker languages of simultaneous bilinguals, or heritage languages. These acquisition types differ from first language development in that onset of acquisition of one language is delayed or that the amount of exposure to one language is reduced. I will argue that age at onset and severely reduced amount of exposure are potential causal factors triggering divergent developments, whereas bilingualism on its own is not a sufficient cause of divergence.

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 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)0.000
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.311
Teacher spread0.292 · 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 teacher head, not a consensus.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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