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Record W4242183497 · doi:10.31234/osf.io/v8r7j

Vois-tu le kem? Do you see the bos? Foreign word learning at 14-months

2021· preprint· en· W4242183497 on OpenAlexafffund
Chelsea da Estrela, Krista Byers‐Heinlein

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWord (group theory)SentenceLinguisticsWord learningContext (archaeology)Computer scienceForeign languageTask (project management)Natural language processingArtificial intelligencePsychologyHistoryVocabularyPhilosophy

Abstract

fetched live from OpenAlex

How easily can infants regularly exposed to only one language begin to acquire a second one? In three experiments, we tested 14‐month‐old English and French monolingual infants’ ability to learn words presented in foreign language sentence frames. Infants were trained on two novel word‐object pairings and then tested using a preferential looking task. Word forms were phonetically and phonotactically legal in both languages, and cross‐spliced across conditions, so only the sentence frames established the word as native or foreign. In Experiment 1, infants were taught one native and one foreign word and successfully learned both. In Experiment 2 and 3, infants were taught two foreign words, but only showed successful learning of the first word they encountered. These results demonstrate that infants can successfully learn words embedded in foreign language sentences, but this is more challenging than native word learning. More broadly, they show that the sentential context of a novel word, and not just the word form itself, influences infants’ early word learning.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.001

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes2
Has abstractyes

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