Getting Early Childhood onto the Reform Agenda: An Australian Case Study
Bibliographic record
Abstract
Abstract Many Governments around the world turned their attention to early childhood policy and service provision throughout the early 2000s, and the Victorian State Government within Australia was no different. Over the past six years, the Victorian Government has reformed the early childhood education and care system substantially. The factors leading to this emphasis on early childhood reform were not straight-forward. This article adapts Richmond’s and Kotelchuck’s (1983) model for examining the interacting forces shaping public policy to make sense of how early childhood so successfully found itself at the centre of the Victorian Government’s reform agenda. It concludes that far from being random, it was a combination of political will, a rich, expanding and interdisciplinary knowledge base and a well-developed social strategy for the application of the knowledge base that led to early childhood being at the centre of the reform agenda. It concludes that much of the evidence by which the reform agenda was informed came from studies conducte d internationally and that more Australian research is needed to investigate early childhood program effectiveness in our own local context if the momentum is to continue.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".