Is Australia Ready to Constitutionally Recognise Indigenous Peoples as Equals?
Bibliographic record
Abstract
This collection of essays explores the history and current status of proposals to recognise Aboriginal and Torres Strait Islander Peoples in the Constitution of Australia. The book had its genesis in a colloquium co-hosted by the University of Southern Queensland and Southern Cross University, attended by scholars from Australia and overseas and prominent participants in the recognition debates. The contributions have been updated and supplemented to produce a collection that explores what is possible and preferable from a variety of perspectives, organised into three parts: 'Concepts and Context', 'Theories, Critique and Alternatives', and 'Comparative Perspectives'. It includes work by well-regarded constitutional law scholars and legal historians, as well as analysis built from and framed by Indigenous world views and knowledges. It also features the voices of a number of comparative scholars – examining relevant developments in the United States, Canada, the South Pacific, the United Kingdom, New Zealand and South America. The combined authorship represents 10 universities from across Australia, the United Kingdom, the United States and Canada. The book is intended to be both an accurate and detailed record of this critical step in Australian legal and political history and an enduring contribution to ongoing dialogue, reconciliation and the empowerment of Australia's First Peoples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".