“We Need to Keep Picturing All of the Stuff I Like!”: Three-Year-Old Children’s Perspectives of Their Kindergarten Experiences During Educational Reform
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".