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Record W3163580859 · doi:10.1098/rstb.2021.0089

Across demographics and recent history, most parents sing to their infants and toddlers daily

2021· article· en· W3163580859 on OpenAlexaff
Ran Yan, Ghazal Jessani, Elizabeth S. Spelke, Peter de Villiers, Jill de Villiers, Samuel A. Mehr

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMerck Canada Inc. (Canada)
FundersHarvard Data Science Initiative, Harvard UniversityNational Institutes of HealthDana Foundation
KeywordsSingingPsychologyDevelopmental psychologyDemographicsEthnic groupDemographySociology

Abstract

fetched live from OpenAlex

Abstract Music is universally prevalent in human society and is a salient component of the lives of young families. Here, we studied the frequency of singing and playing recorded music in the home using surveys of parents with infants (N = 945). We found that most parents sing to their infant on a daily basis and the frequency of infant-directed singing is unrelated to parents’ income or ethnicity. Two reliable individual differences emerged, however: (i) fathers sing less than mothers and (ii) as infants grow older, parents sing less. Moreover, the latter effect of child age was specific to singing and was not reflected in reports of the frequency of playing recorded music. Last, we meta-analysed reports of the frequency of infant-directed singing and found little change in its frequency over the past 30 years, despite substantial changes in the technological environment in the home. These findings, consistent with theories of the psychological functions of music, in general, and infant-directed singing, in particular, demonstrate the everyday nature of music in infancy. This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part I)’.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.128
GPT teacher head0.310
Teacher spread0.181 · 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 designBench or experimental
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

Citations72
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePhilosophical Transactions of the Royal Society B Biological SciencesSame topicNeuroscience and Music PerceptionFrench-language works237,207