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Record W2970165057 · doi:10.1002/casp.2438

From empathy to action: Can enhancing host‐society children's empathy promote positive attitudes and prosocial behaviour toward refugees?

2019· article· en· W2970165057 on OpenAlexfundno aff
Laura K. Taylor, Catherine Glen

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsRefugeeEmpathyProsocial behaviorMediationPsychologyAction (physics)Social psychologyDevelopmental psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Over half of refugees are school‐aged children. In host communities, children's attitudes and behaviours are important for the integration of refugee children. This study examines the empathy–attitudes–action model in middle childhood ( N = 94, 8 to 11 years old). In both the experimental and control conditions, children were introduced to a (fictional) refugee and told that he or she would be moving to their school. The experimental condition also listened to a storybook about the child's refugee experience. Empathy, outgroup attitudes, and prosocial behaviour toward the incoming child, and refugees as a group, were measured. Although mediation was not supported, the storybook condition reported more empathy and helping intentions, and attitudes predicted helping intentions but not giving to refugees. Results highlight how host‐society children can welcome refugees.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.383
Teacher spread0.352 · 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

Citations58
Published2019
Admission routes1
Has abstractyes

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