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Record W3214447095 · doi:10.32920/ryerson.14656395.v1

Playing it straight: kindergarten children's perspectives on gender in play materials

2021· preprint· en· W3214447095 on OpenAlexaff
Ameera Ali

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood Development
Fundersnot available
KeywordsFemininityMasculinityPsychologyFlexibility (engineering)PerformativityPerceptionHeteronormativityStereotype (UML)Gender psychologyDevelopmental psychologyGender identityGender studiesGender roleGender equalitySocial psychologyHuman sexualitySociology

Abstract

fetched live from OpenAlex

This study focuses on the perspectives of kindergarten children regarding their perceptions of gender appropriateness of play materials. The theory of gender performativity has been used as a theoretical lens for the study. Six kindergarten children between the ages of four and five were individually interviewed about whether they believed toys to be gender-specific or gender-neutral. Results indicated that children displayed gender-stereotype knowledge as well as an understanding that toys can be gender-neutral, however, they were generally perceived to be gender-specific. Themes found include: perceptions of play materials as gender-neutral, perceptions of play materials as male-appropriate, perceptions of play materials as female-appropriate, notions of gender stereotypes, gender-role flexibility, contingent gender-role flexibility and external knowledge sources. Implications of these results regarding both gender-conforming as well as gender-non conforming children are provided and recommendations for educators are suggested. Keywords: gender performativity; children; masculinity; femininity; heteronormativity; toys; gender stereotypes, gender roles, gender non-conforming behaviour

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.005
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.320
Teacher spread0.273 · 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 designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

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