MétaCan
Menu
Back to cohort
Record W3111660588 · doi:10.1177/0023830920977050

The Processing of Spanish Article–Noun Gender Agreement by Monolingual and Bilingual Toddlers

2020· article· en· W3111660588 on OpenAlexafffund
Monika Molnar, José Alemán Bañón, Simona Mancini, Sendy Caffarra

Bibliographic record

VenueLanguage and Speech · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEusko JaurlaritzaEuropean Commission
KeywordsPsychologyLinguisticsToddlerAgreementNoun phraseActive listeningNounPreferenceNeuroscience of multilingualismDevelopmental psychologyCommunicationMathematics

Abstract

fetched live from OpenAlex

We assessed monolingual Spanish and bilingual Spanish-Basque toddlers’ sensitivity to gender agreement in correct vs. incorrect Spanish noun phrases (definite article + noun), using a spontaneous preference listening paradigm. Monolingual Spanish-learning toddlers exhibited a tendency to listen longer to the grammatically correct phrases (e.g., la casa; “the house”), as opposed to the incorrect ones (e.g., * el casa). This listening preference toward correct phrases is in line with earlier results obtained from French monolingual 18-month-olds (van Heugten & Christophe, 2015). Bilingual toddlers in the current study, however, tended to listen longer to the incorrect phrases. Basque was not a source of interference in the bilingual toddler’s input as Basque does not instantiate grammatical gender agreement. Overall, our results suggest that both monolingual and bilingual toddlers can distinguish between the correct and incorrect phrases by 18 months of age; however, monolinguals and bilinguals allocate their attention differently when processing grammatically incorrect forms.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.281
Teacher spread0.263 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueLanguage and SpeechSame topicLanguage Development and DisordersFrench-language works237,207