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Record W2951221045 · doi:10.1386/ijmec.14.1.9_1

Nurturing infants with music

2019· article· en· W2951221045 on OpenAlexafffund
Sandra E. Trehub

Bibliographic record

VenueInternational Journal of Music in Early Childhood · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSingingPsychologyGestureMelodyDevelopmental psychologyNature versus nurtureArousalAffectionMoodMusicalSocial psychology

Abstract

fetched live from OpenAlex

Primary caregivers throughout the world provide infants with life-sustaining care such as nutrition and protection from harm as well as life-enhancing care such as affection, contingent responsiveness and mentoring of various kinds. They nurture infants musically by means of one-on-one (i.e. infant-directed) singing accompanied by movement in some cultures and by visual gestures in others. Such singing, which is acoustically and visually distinct from solitary (i.e. self-directed) singing, is effective in engaging infants and regulating their mood and arousal. The repetition and stereotypy of caregivers’ performances contribute to their memorability and dyadic significance. Caregivers’ singing also influences infants’ social engagement more generally. Once infants become singers, their songs play an important role in social interaction and emotional self-regulation. Although caregivers sing to infants with playful or soothing intentions, their performances highlight the temporal and melodic structure of the music. In sum, caregivers lay the foundation for a lifelong musical journey.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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

Citations41
Published2019
Admission routes2
Has abstractyes

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