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Record W4405124654 · doi:10.1111/infa.12641

Infants' Knowledge of Individual Words: Investigating Links Between Parent Report and Looking Time

2024· article· en· W4405124654 on OpenAlexafffund
Melanie López Pérez, Charlotte E. Moore, Andrea Sander‐Montant, Krista Byers‐Heinlein

Bibliographic record

VenueInfancy · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyActive listeningVocabularyTask (project management)Word (group theory)Cognitive psychologyWord recognitionVocabulary developmentDevelopmental psychologyLinguisticsCommunicationTeaching methodReading (process)Mathematics education

Abstract

fetched live from OpenAlex

Assessing early vocabulary development commonly involves parent report methods and behavioral tasks like looking-while-listening. While both yield reliable aggregate scores, findings are mixed regarding their reliability in measuring infants' knowledge of individual words. Using archival data from 126 monolingual and bilingual 14-31-month-olds, we further examined links across these methods at the word level, while controlling for potentially confounding child-level factors. When data were averaged at the child level, performance on the looking-while-listening task correlated well with parent-reported word production of the same words, as expected. However, mixed-effects model comparisons suggested that at the word level, looking-while-listening performance was significantly predicted by age and total productive vocabulary, but not by parent-reported knowledge of a word once these factors were controlled for. These findings invite careful consideration regarding the adequacy of these two popular methods for capturing children's idiosyncratic knowledge of individual words.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.335
Teacher spread0.308 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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