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Record W4297270910 · doi:10.1111/jasp.12917

Singing foreign songs promotes shared common humanity in elementary school children

2022· article· en· W4297270910 on OpenAlexafffund
Arla Good, Kathleen F. Peets, Becky L. Choma, Frank Russo

Bibliographic record

VenueJournal of Applied Social Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSingingOutgroupPsychologyHumanityIdentity (music)Prejudice (legal term)Ingroups and outgroupsSocial psychologyProsocial behaviorDevelopmental psychologyAestheticsArtAcousticsPolitical science

Abstract

fetched live from OpenAlex

Abstract Several studies support the notion that singing the songs of a foreign culture can reduce prejudice and improve intergroup relations in elementary school children. The current article presents theoretical and empirical support for one potential mechanism underpinning this effect, namely, that singing songs may highlight commonalities and generate a collective identity across intergroup boundaries. Twenty‐nine elementary school children (mean age = 10.73 years) in two predetermined groups participated in a 6‐week, crossover study in which they received two singing interventions: (1) singing songs from their own (ingroup) culture and (2) singing songs from foreign (outgroup) cultures. Quantitative and qualitative data analyses demonstrate that singing foreign songs led to higher levels of perceived commonality toward outgroup others and promoted the adoption of a collective identity. Furthermore, interviews elucidated that singing foreign songs encouraged children to appreciate the unique identity of each culture while acknowledging their shared common humanity, suggesting the cultivation of a dual identity. Theoretical and practical implications of these results are discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.359
Teacher spread0.329 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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