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Record W7116120994 · doi:10.1007/s13158-025-00471-z

“We Need to Keep Picturing All of the Stuff I Like!”: Three-Year-Old Children’s Perspectives of Their Kindergarten Experiences During Educational Reform

2025· article· en· W7116120994 on OpenAlexaff

Bibliographic record

VenueInternational Journal of Early Childhood · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsEducation and Early Childhood Development
FundersPaul Ramsay FoundationIan Potter FoundationDavid and Elaine Potter Foundation
KeywordsEarly childhood educationEarly childhoodQualitative researchContent analysisGovernment (linguistics)Professional developmentChild developmentSemi-structured interview

Abstract

fetched live from OpenAlex

Abstract Following government’s significant investment in 3- and 4-year-old kindergarten in Victoria, Australia, the Educational and Developmental Gains in Early Childhood (EDGE) Study investigated the impact of 2 years of funded kindergarten on the early childhood education workforce, and children’s learning and development. EDGE’s Professional Practice and Learning Experiences domain documented the perspectives and experiences of teachers and leaders implementing 3-year-old kindergarten in Victoria, as well as those of children, families, and government agents involved in the program’s implementation. This study focused on exploring children’s perspectives of 3-year-old kindergarten during the early roll out in Victoria. Framed within bioecological theory, a qualitative mosaic approach was used. Data were collected from 14 3-year-old children using child perspectives journals which included an interview, drawing and narration, and photography. A content analysis was applied to the data. Children expressed their experiences in relation to play, physical environments and materials, and social relationships. Findings support conceptions that children are capable social actors in kindergarten programs, and their perspectives are key to building rich insights on 3-year-old kindergarten.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.278
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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