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Record W2176097749 · doi:10.1111/cdev.12442

Implicit Racial Biases in Preschool Children and Adults From Asia and Africa

2015· article· en· W2176097749 on OpenAlexafffund
Miao Qian, Gail D. Heyman, Paul C. Quinn, Francoise A. Messi, Genyue Fu, Kang Lee

Bibliographic record

VenueChild Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPsychologyRacial biasRace (biology)Developmental psychologyImplicit-association testImplicit attitudeImplicit biasRacial differencesRacismRacial groupSocial cognitionEthnic groupSocial psychologyCognitionGender studies

Abstract

fetched live from OpenAlex

This research used an Implicit Racial Bias Test to investigate implicit racial biases among 3- to 5-year-olds and adult participants in China (N = 213) and Cameroon (N = 257). In both cultures, participants displayed high levels of racial biases that remained stable between 3 and 5 years of age. Unlike adults, young children's implicit racial biases were unaffected by the social status of the other-race groups. Also, unlike adults, young children displayed overt explicit racial biases, and these biases were dissociated from their implicit biases. The results provide strong evidence for the early emergence of implicit racial biases and point to the need to reduce them in early childhood.

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)0.003
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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

Citations89
Published2015
Admission routes2
Has abstractyes

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