Engaging Indigenous Parents in Preparing their Children for School. Australian Research Alliance for Children and Youth
Bibliographic record
Abstract
his paper examines the evidence showing high levels of vulnerability in the Indigenous population from the early years of life and span physical, social and cognitive areas of development. For many of these Indigenous children this early vulnerability will impact on their ability to do well at school; most will find learning extremely difficult, and their general health, social and emotional wellbeing and education outcomes are likely to limit their opportunities later in life. At the same time, the strengths of Indigenous children, parents, extended families and communities are often overlooked and diminished by inappropriate interventions, unintended outcomes of policy decisions and inexperienced (albeit well intentioned) practitioners. There is widespread system failure underpinned by dominant paradigms and bureaucratic inertia that results in a lack of access to resources and services, unrealistic funding cycles, short program implementation timeframes, and inappropriate performance measures to evaluate program effectiveness. System-wide racism and misunderstanding foster feelings of mistrust and betrayal among Indigenous communities and fuel the effects of transgenerational loss and dysfunction experienced by thousands of Indigenous families. The paper examines the research and practice evidence relating to engaging parents and families in early development and the process of school readiness, and draws on the knowledge, wisdom and practice experience of Indigenous people.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".