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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 score0.228
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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