First Nations Voices in Child Protection Decision Making: Changing the Frame
Bibliographic record
Abstract
First Nations children in Australia remain vastly over-represented in the child protection (CP) and out-of-home care (OOHC) systems, and in juvenile detention and adult incarceration systems. To change this, we need to tackle the problem at the source; by maintaining our efforts for the implementation of First Nations rights, so that self-determination and cultural safety are embedded into the child protection system from a family’s first contact and by constantly identifying opportunities in the current system to keep our children safe. Using policy and research literature, this paper identifies the principal barrier to change as the continuing failure of settler governance to recognise the fundamental importance of First Nations rights, including the need to embed self-determination and a specific, First Nations cultural framework into the child protection system. The article also offers personal reflections on the essential role of self-determination in keeping our children safe, drawing on Aunty Glendra Stubbs’ experiences in community-based advocacy and support of families for nearly three decades. Her reflections are linked to the literature and First Nations advocacy that support the findings and opportunities for change proposed in this paper.
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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 | 0.073 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.072 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.012 | 0.018 |
| 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".