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Record W4384037099 · doi:10.26522/brocked.v32i2.976

Early childhood educators reflect on their conversations with parents about children’s diverse gender expression

2023· article· en· W4384037099 on OpenAlexaffvenueabout
Sarah Reddington

Bibliographic record

VenueBrock Education Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsGirlPsychologyDevelopmental psychologyExpression (computer science)Diversity (politics)Early childhoodGender studiesEarly childhood educationTransgenderSociology

Abstract

fetched live from OpenAlex

This research captures early childhood educators (ECEs) perspectives when communicating with parents about their children’s diverse gender expression. Since families and ECEs play a pivotal role in shaping young children’s understandings of gender, there is requirement to learn more about ECEs communications with parents. The research that informs this paper is derived from semi-structured focus groups with 15 ECEs who work with young children, ages 3-5 years, at regulated early childhood centers in Halifax, Nova Scotia, Canada. It explores the reflections from ECEs after having conversations with parents whose children identify outside the traditional constructs of masculine boy/ feminine girl. One central finding the ECEs observe is the displeasure fathers have when their sons engage in feminine interests, including the affective actions the fathers then take to regulate and remove stereotypical girl activities from their sons’ lives. This research highlights the need for more early childhood education training on gender diversity to better support non-binary, transgender children, and children from LGBTQ families and future collaboration with families.

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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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