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Record W4387002634 · doi:10.1037/dev0001590

Children’s implicit gender–toy association development varies across cultures.

2023· article· en· W4387002634 on OpenAlexafffundabout
Miao Qian, Wang Ivy Wong, A. Natisha Nabbijohn, Yang Wang, Laura N. MacMullin, Haley J James, Genyue Fu, Bin Zuo, Doug P. VanderLaan

Bibliographic record

VenueDevelopmental Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto MississaugaNational Natural Science Foundation of China
KeywordsPsychologyPsycINFODevelopmental psychologyStereotype (UML)Association (psychology)ChinaChild developmentSocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

= 1,013; 49.70% girls) in Canada, China, and Thailand. Children from all three cultures evidenced implicit gender-toy stereotypes over this developmental period, but cultural differences in the developmental pattern and strength of these stereotypes were apparent. Gender-toy stereotypes were relatively strong and stable across age groups among Thai children and relatively weak and stable across age groups among Chinese children. Canadian 4- to 5-year-old children displayed weaker stereotypes, whereas 6- to 9-year-olds displayed stronger stereotypes. These findings highlight the contribution of culture to children's gender stereotype development. Although gender-toy stereotypes were found among 4- to 9-year-olds in all three cultures examined here, the strength of these stereotypes varies by culture. Furthermore, the previously described increase in gender stereotyping over this developmental period appears to not apply across cultures, thus challenging the conventional view on development in this domain based on prior, mainly Western, research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.051
GPT teacher head0.391
Teacher spread0.340 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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