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Record W4281671173 · doi:10.7203/leeme.49.24089

Habilidades musicales y cognitivas en niños/as de entornos desfavorecidos

2022· article· en· W4281671173 on OpenAlexaboutno aff
Graça Boal-Palheiros, Pedro Figueira, São Luís Castro

Bibliographic record

VenueRevista Electrónica de LEEME · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsPsychologyDisadvantagedMusicalCognitionMemory spanDevelopmental psychologyWorking memoryArt

Abstract

fetched live from OpenAlex

Human beings are born with several abilities which are amenable to change. A growing number of studies have focused on the relations between musical and cognitive abilities but research with children from disadvantaged communities is scarce. This study explored the relations between musical and cognitive abilities in disadvantaged children. Participants were 169 children from deprived neighborhoods, attending the second year of primary education in public schools that do not offer music education. Children’s musical abilities (perception) were measured with the Melody, Rhythm, and Memory tests of Montreal Battery for the Evaluation of Musical Abilities and their cognitive abilities, with five WISC-III subtests (Similarities, Vocabulary, Cubes, Picture Arrangement, and Digits). Parental level of education was obtained from a questionnaire on the socio-economic status of children’s families. Results revealed (1) few and weak correlations between musical and cognitive abilities; (2) stronger correlations of Socioeconomic Status (SES) with cognitive than with musical abilities; (3) digit span predicts all musical abilities; 4) a clear factorial distinction between musical and cognitive abilities. Overall, results suggest that disadvantaged children’s musical and cognitive abilities, as measured by the present instruments, are partly independent regarding processing components.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0330.001

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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevista Electrónica de LEEMESame topicDiverse Music Education InsightsFrench-language works237,207