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Lexical repertoire of 24 and 30-month-old children speaking Brazilian portuguese: preliminary results

2024· article· en· W4398152517 on OpenAlexaff
Carolina Felix Providello, Ana Paola Nicolielo Carrilho, Vânia Peixoto, Fátima Maia, Simone Rocha de Vasconcellos Hage

Bibliographic record

VenueCoDAS · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsRepertoirePortugueseBrazilian PortugueseLinguisticsPsychologyHistorySociologyCommunicationArtLiteraturePhilosophy

Abstract

fetched live from OpenAlex

ABSTRACT Purpose To check the lexical repertoire of Brazilian Portuguese-speaking children at 24 and 30 months of age and the association between the number of words spoken and the following variables: socioeconomic status, parents’ education, presence of siblings in the family, whether or not they attend school, and excessive use of tablets and cell phones. Methods 30 parents of children aged 24 months living in the state of São Paulo participated in the study. Using videoconferencing platforms, they underwent a speech-language pathology anamnesis, an interview with social services, and then they completed the “MacArthur Communicative Development Inventory - First Words and Gestures” as soon as their children were 24 and 30 months old. Quantitative and qualitative inferential inductive statistics were applied. Results the median number of words produced was 283 at 24 months and 401 at 30 months, indicating an increase of around 118 words after six months. The child attending a school environment had a significant relationship with increased vocabulary. Conclusion The study reinforces the fact that vocabulary grows with age and corroborates the fact that children aged 24 months already have a repertoire greater than 50 words. Those who attend school every day produce at least 70 more words than those who do not.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.882
Threshold uncertainty score0.855

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)0.0010.000

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 head0.015
GPT teacher head0.290
Teacher spread0.276 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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