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Record W2006430088 · doi:10.1525/mp.2011.29.2.157

Music Training and Reading Readiness

2011· article· en· W2006430088 on OpenAlexaff
Christine D. Tsang, Nicole J. Conrad

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsSaint Mary's UniversityWestern University
Fundersnot available
KeywordsReading (process)PsychologyAssociation (psychology)Music perceptionPhonological awarenessPerceptionCognitive psychologyAuditory perceptionCognitionMusic educationDevelopmental psychologyLinguisticsPedagogy

Abstract

fetched live from OpenAlex

several reports have noted significant associations among phonological awareness, early reading skills, and music perception skills in young children. We examined whether music processing skills differentially predicted reading performance in a broad age range of 69 children with and without formal music training. Pitch perception was correlated with phonological awareness, a finding consistent with the hypothesis that basic auditory processing skills underlie the association between music and reading abilities. Nevertheless, the correlation between music skills and reading skills was affected by the presence of formal music training: pitch discrimination predicted reading ability only in children without formal music training. Studies examining the association between music perception and reading (and perhaps other cognitive domains as well) should not ignore the factor of music training.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0050.001

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 head0.167
GPT teacher head0.343
Teacher spread0.176 · 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

Citations61
Published2011
Admission routes1
Has abstractyes

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