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Record W2977699227 · doi:10.1111/infa.12296

Monolingual and bilingual infants’ word segmentation abilities in an inter‐mixed dual‐language task

2019· article· en· W2977699227 on OpenAlexafffund
Adriel John Orena, Linda Polka

Bibliographic record

VenueInfancy · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and Music
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyFirst languageContext (archaeology)Task (project management)Text segmentationLinguisticsDual languageWord (group theory)Neuroscience of multilingualismSegmentationArtificial intelligenceComputer scienceGeography

Abstract

fetched live from OpenAlex

Previous studies show that young monolingual infants use language-specific cues to segment words in their native language. Here, we asked whether 8 and 10-month-old infants (N = 84) have the capacity to segment words in an inter-mixed bilingual context. Infants heard an English-French mixed passage that contained one target word in each language, and were then tested on their recognition of the two target words. The English-monolingual and French-monolingual infants showed evidence of segmentation in their native language, but not in the other unfamiliar language. As a group, the English-French bilingual infants segmented in both of their native languages. However, exploratory analyses suggest that exposure to language mixing may play a role in bilingual infants' segmentation skills. Taken together, these results indicate a close relation between language experience and word segmentation skills.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.307
Teacher spread0.297 · 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

Citations28
Published2019
Admission routes2
Has abstractyes

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