Building capacity in indigenous governance: Comparing the Australian and American experiences
Bibliographic record
Abstract
Abstract This paper compares key aspects of governance structures for Indigenous populations in the United States and Australia. The paper focuses on policy coordination and administration, in particular the nodes of decision‐making in the two countries in relation to government contracting and accountability. The U.S. approach to funding Indigenous organizations stems from the 1975 Indian Self‐Determination and Education Act and its subsequent expansions. Through the development of contracting into permanent compacting via block grants, this approach builds established nodes of Indigenous government and facilitates whole‐of‐government coherence at the level of the American Indian tribe. The U.S. approach seems correlated with better performance and may lighten bureaucratic loads over the long term. The Australian model, on the other hand, seeks to create whole‐of‐government coherence through top‐down financial accountability in a way that hampers the development of Indigenous political capacity. The paper traces the development of these practices through time and illustrates how they contribute to the fragmentation rather than growth of Indigenous political capacities. It suggests ways the Australian model could be improved even in the absence of fundamental reform by drawing on the contracting‐to‐compacting framework of longstanding U.S. practices.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".