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Record W2611152539 · doi:10.1037/dev0000290

Perceptual individuation training (but not mere exposure) reduces implicit racial bias in preschool children.

2017· article· en· W2611152539 on OpenAlex
Miao Qian, Paul C. Quinn, Gail D. Heyman, Olivier Pascalis, Genyue Fu, Kang Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDevelopmental Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthNational Institutes of Natural SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPsychologyPsycINFOIndividuationDevelopmental psychologyPerceptionRace (biology)Cognitive psychologyMEDLINE

Abstract

fetched live from OpenAlex

Two studies with preschool-age children examined the effectiveness of perceptual individuation training at reducing racial bias (Study 1, N = 32; Study 2, N = 56). We found that training preschool-age children to individuate other-race faces resulted in a reduction in implicit racial bias while mere exposure to other-race faces produced no such effect. We also showed that neither individuation training nor mere exposure reduced explicit racial bias. Theoretically, our findings provide strong evidence for a causal link between individual-level face processing and implicit racial bias, and are consistent with the newly proposed perceptual-social linkage hypothesis. Practically, our findings suggest that offering children experiences that allow them to increase their expertise in processing individual other-race faces will help reduce their implicit racial bias. (PsycINFO Database Record

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.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.160
GPT teacher head0.414
Teacher spread0.254 · 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