Recommendations on the Optimal Constitutional Recognition of the First Nations in Australia
Bibliographic record
Abstract
This note extends my previous analysis of the constitutional recognition of Aboriginal and Torres Strait Islander Peoples (‘First Nations’) by providing guidance on the optimal approach for this recognition. The guidance is founded on the concepts of efficiency and equity. An optimal recognition is defined as one that achieves both objectives simultaneously. Efficiency flows from a dynamic recognition that changes over time relatively easily, as exemplified by a treaty-based approach. The equity criterion has, as a proxy, legal pluralism, whereby constitutional recognition enlivens ‘Indigenous jurisprudence’ through mechanisms such as self-governance. The proposal is to combine efficiency and equity by guaranteeing the collective rights of Indigenous Australians in accordance with universally recognised principles and norms of international law, such as the UN Declaration on the Rights of Indigenous Peoples (for which the Commonwealth of Australia announced its support in 2009). This in turn is likely to guide a treaty-based approach to the relationship between the Commonwealth and First Nations that can evolve towards legal pluralism.
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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 | 0.049 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.031 | 0.028 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".