Bibliographic record
Abstract
The leading book on the treaty debate in Australia has been fully revised. This second edition takes a fresh look at modern treaty-making between Indigenous peoples and governments in Australia. Exploring the why, where, and how of treaty, it concludes by offering seven strategies for achieving treaty. \n \nA number of significant developments have occurred since the publication of the first edition. In Australia, key events include the emergence of State and Territory driven treaty processes, the negotiation and finalisation of the Noongar Settlement, and the delivery of the Uluru Statement from the Heart. International and comparative standards also continue to evolve. In 2007, the United Nations General Assembly adopted the Declaration on the Rights of Indigenous Peoples, while Canada and New Zealand continue to negotiate a range of claims involving land and other points of difference. \n \nTreaty presents readers with everything they need to know about treaties, from the basic question of “what is a treaty?” to “how have other countries negotiated treaties?”. It challenges the reader to question whether Australia should go down the treaty path; a path that could lead to political settlements that empower Aboriginal and Torres Strait Islander peoples and address the injustices at the heart of the Australian state
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.052 |
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".