Support for the Early Years and Australia’s Future Health: The Australian Early Development Index
Bibliographic record
Abstract
Substantial evidence supports the early years of child development as crucial to setting the foundation for competence and coping skills that will affect learning, behaviour and health throughout the life course. The subject of early child development must be a high priority for communities, and their governments- from macro policy development to local service delivery. With resources scarce it is vital that all policies and services for children and families are based on solid evidence. This evidence needs to be of high quality, replicable for benchmarking, freely available to all residing and working in the community and reflect the breadth of child development. The Canadian designed Early Development Index (EDI) is a population level instrument that measures five developmental domains: language and cognitive skills, emotional maturity, physical health and well-being, communication skills/general knowledge and social competence. The EDI provides a scorecard for communities interested in learning what is going right and wrong for their children. It also provides evidence that communities can use to advocate for improvement of programs and facilities relevant to the early years. In 2002 the North Metropolitan Health Service (NMHS) piloted the EDI in seven suburbs, the first time the EDI had been utilised outside Canada. Positive results led to the development of Communities for Early
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".