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Record W4253786705 · doi:10.32920/ryerson.14663211

Moving toward gender fluid discourses in early childhood settings: ECEs share their experiences and ideas

2021· preprint· en· W4253786705 on OpenAlexaff
Chloe Waters

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsToronto Metropolitan UniversityUniversity of WinnipegEducation and Early Childhood Development
Fundersnot available
KeywordsPerspective (graphical)Early childhood educationQueerGender studiesFocus groupQueer theorySociologyQualitative researchPedagogySocial justiceEarly childhoodPsychologyDevelopmental psychologySocial science

Abstract

fetched live from OpenAlex

The dominant, yet dated discourse surrounding gender has been discussed primarily using developmental theory. Over the last thirty years, scholars have been challenging this discourse, but this is often not reflected in practice. This qualitative study is informed by a phone interview and a focus group session with educators, who believe that adopting a more gender fluid perspective with children is important. Inspired by Queer theory, employing a critical paradigm and social justice framework, it investigates how a queering of current gender discourses is being incorporated into ECEC practice. By consulting educators, the research gains insight on how gender fluid discourses can be incorporated into the field of ECEC through learning how educators are already incorporating gender fluid discourses in a proactive manor with preschool age children in ECEC settings. In the findings, five main themes were identified focusing on materials, practices, parents, ECEs, and ECE education and support. Keywords: early childhood education and care, educators, gender fluid, reconceptualizing

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)0.010
metaresearch head score (Gemma)0.013
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 score0.012
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.024
Scholarly communication0.0110.010
Open science0.0010.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.297
Teacher spread0.263 · 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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