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Record W4409128898 · doi:10.1007/s10643-025-01907-w

Implementing 3-Year-Old Kindergarten in Victoria: Teachers’, Educators’ and Directors’ Perspectives

2025· article· en· W4409128898 on OpenAlexaff
Jane Page, Laura McFarland, Sarah Young, Lisa M. Baker, Penny Levickis, Tricia Eadie

Bibliographic record

VenueEarly Childhood Education Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsEducation and Early Childhood Development
FundersUniversity of Melbourne
KeywordsSociology of EducationEarly childhood educationPsychologyPedagogyPreschool educationMathematics educationSociology

Abstract

fetched live from OpenAlex

Abstract In the past two decades, Australian governments have initiated a series of reforms to provide accessible, affordable, high quality early childhood education and care (ECEC). At the core of these reforms is the intention to improve learning outcomes for young children in the years where strong foundations are laid and where learning inequalities can be addressed. The Victorian State Government has built on these reforms through its investment in rolling out two years of funded universal kindergarten for 3-and 4-year-old children. This qualitative study reports on teachers’, educators’ and directors’ experiences of implementing funded 3-year-old kindergarten at 26 ECEC services from metropolitan and regional/rural Victorian regions. Online semi-structured interviews were conducted with 44 participants in 2022–2023. Thematic analysis revealed key findings, including the importance of pedagogical differentiation, workforce sustainability, leading and responding to change, and building capacity, as well as the structural impacts on implementation. We conclude that participants’ perspectives and experiences are critical to understanding the optimal opportunities and conditions for 3-year-old children’s learning across diverse contexts in Victoria during a time of reform.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.314
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.292
Teacher spread0.285 · 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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