Bibliographic record
Abstract
During the past decade, a remarkable transference of responsibility to Indigenous children’s organisation has taken place in many parts of Australia, Canada, the USA and New Zealand. It has been influenced by Indigenous peoples’ human rights advocacy at national and international levels, by claims to self-determination and by the globalisation of Indigenous children’s organisations. Thus far, this reform has taken place with little attention from academic and non-Indigenous communities; now, Decolonising Indigenous Child Welfare: Comparative Perspectives considers these developments and, evaluating law reform with respect to Indigenous child welfare, asks whether the pluralisation of responses to their welfare and well-being, within a cross-cultural post-colonial context, can improve the lives of Indigenous children. The legislative frameworks for the delivery of child welfare services to Indigenous children are assessed in terms of the degree of self-determination which they afford Indigenous communities. The book draws upon interdisciplinary research and the author’s experience collaborating with the peak Australian Indigenous children’s organisation for over a decade to provide a thorough examination of this international issue. Dr Terri Libesman is a Senior Lecturer in the Law Faculty, at the University of Technology Sydney. She has collaborated, researched and published for over a decade with the peak Australian Indigenous children’s organisation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".