Evidence map›Paper›PMID 34468071›Full record

ArticleDevelopmental science2022

Who is a typical woman? Exploring variation in how race biases representations of gender across development.

Rachel A Leshin, Ryan F Lei, Magnolia Byrne, Marjorie Rhodes

Abstract read
In one paragraph

Article in Developmental science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rachel A LeshinDepartment of Psychology, New York University, New York, USA.ORCID 0000-0002-4501-9085
Ryan F LeiDepartment of Psychology, Haverford College, Haverford, Pennsylvania, USA.
Magnolia ByrneDepartment of Psychology, New York University, New York, USA.
Marjorie RhodesDepartment of Psychology, New York University, New York, USA.

Funding

“The Linguistic Transmission of Maladaptive Beliefs”R01HD087672 · NICHD · NEW YORK UNIVERSITY · PI RHODES, MARJORIE · 2016 to 2020
$1.8M
NICHD NIH HHS R01 HD087672
6 · The paper itself

Abstract

From early in development, race biases how children think about gender-often in a manner that treats Black women as less typical and representative of women in general than White or Asian women. The present study (N = 89, ages 7-11; predominately Hispanic, White, and multi-racial children) examined the generalizability of this phenomenon across middle childhood and the mechanisms underlying variability in its development. Replicating prior work, children were slower and less accurate to categorize the gender of Black women compared to Asian or White women, as well as compared to Black men, suggesting that children perceived Black women as less representative of their gender. These effects were robust across age within a racially and ethnically diverse sample of children. Children's tendencies to view their own racial identities as expansive and flexible, however, attenuated these effects: Children with more flexible racial identities also had gender concepts that were more inclusive of Black women. In contrast, the tendency for race to bias children's gender representations was unrelated to children's multiple classification skill and racial essentialism. These findings shed light on the mechanisms underlying variation in how race biases gender across development, with critical implications for how children's own identities shape the development of intergroup cognition and behavior.

Indexed as

RacismBlack PeopleChildFemaleGender IdentityHispanic or LatinoHumansMaleSexismcategorizationgenderidentityintersectionalityracesocial cognition

Identifiers

PMID34468071
PMCPMC8847246

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.