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Record W2168662558 · doi:10.20360/g2zk5x

Singing is a Celebration of Language: Using Music to Enhance Young Children’s Vocabularies

2014· article· en· W2168662558 on OpenAlexaffvenue
Kari‐Lynn Winters, Shelley M. Griffin

Bibliographic record

VenueLanguage and Literacy · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsBrock University
Fundersnot available
KeywordsSingingImprovisationMusicalSoundscapeVocabularyLanguage acquisitionMusical developmentLinguisticsPsychologyCommunicationVisual artsArtSound (geography)Mathematics educationAcoustics

Abstract

fetched live from OpenAlex

Music engages children in language learning, offering them opportunities to understand and express their ideas and communicate with others in ways that go beyond words. This article, based on two ethnographically-framed studies and the use of two real-life vignettes, demonstrates how singing and musical experiences (e.g., composition, soundscapes, musical improvisation) have the power to enhance children’s lexical acquisition and semantic knowledge at various levels of development. Results demonstrate that singing and musical experiences, whether biologically or socially shaped, provide opportunities to celebrate language and enhance young children’s vocabulary building.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.289
Teacher spread0.278 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

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