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Record W4399805208 · doi:10.1386/ijmec_00062_1

Vocal pitch matching in early childhood as a relative cognitive strength among low academic performers

2023· article· en· W4399805208 on OpenAlexaff
Timothy T. Brown, Sarah C. Dowling, Margie Orem, David Gonzalez-Maldonado, Naomi T. Lin, Hilda Parra, Setu Shiroya, Steven M. Davis, Matthew J. Doyle, Terry L. Jernigan, John R. Iversen

Bibliographic record

VenueInternational Journal of Music in Early Childhood · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMatching (statistics)PsychologyCognitionMathematicsStatisticsNeuroscience

Abstract

fetched live from OpenAlex

In this study, using some novel measures, we examined vocal pitch matching in 4- and 5-year-old children in transitional kindergarten classrooms and, at two time points, tested relationships between children’s singing pitch accuracy and their classroom grades as well as performance on standardized measures of developing cognitive and academic skills. Consistent with previous studies, children’s grades were strongly correlated with their performance on standardized measures and differed significantly by gender, maternal education, household income and household language. In contrast, vocal pitch matching and tonal pitch processing showed no consistent relationship to grades, standardized tests or sociodemographic variables, and children with lower academic performance showed statistically equivalent pitch singing on average compared to their peers with higher grades. These findings suggest pitch processing and production abilities may be a relative cognitive strength among children doing less well in school, which may be explored for developing programmes to lift their academic performance.

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)0.000
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.286
Teacher spread0.266 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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