Settling With Indigenous People: Modern Treaty and Agreement-Making
Bibliographic record
Abstract
'Settling With Indigenous People' is an edited collection that describes the making of ten contemporary, mostly Australian, local and regional agreements and details the avenues through which such agreements can be implemented and sustained. The Australian regional agreements concern South West Australia, the Murray-Darling Basin, and Cape York. There is a chapter about the return of the Maralinga lands to its traditional owners and one detailing two local government agreements in central and southwest Australia. Urban agreements in Darwin and Vancouver are compared and there are also chapters on the North West Territories and Northern Quebec in Canada and the Ngai Tahu in the South Island of New Zealand. The discussion addresses governance and leadership, negotiation strategies, including the role of formal negotiating frameworks, the importance of process and outcome, the crucial impact of politics and timing, the significance of private sector engagement, and implementation mechanisms. The chapters show how agreement-making has provided a forum in which indigenous groups can negotiate their needs and aspirations, including fundamental issues of recognition, inclusion and economic opportunity. The authors include indigenous and non-indigenous academics, and others who have been involved in negotiating agreements.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.065 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| 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".