A comparative analysis of constitutional recognition of Aboriginal peoples
Bibliographic record
Abstract
This article furnishes a comparative analysis on the constitutional recognition of Indigenous peoples in four jurisdictions. The analysis looks at two jurisdictions that share a similar colonial heritage with Australia, namely New Zealand and Canada; and two jurisdictions at the forefront of plurinational constitutional recognition of Indigenous rights (Ecuador and Bolivia). Experience in these countries suggests that constitutional recognition (of Indigenous peoples) occurs in a variety of ways, including the protection and promotion of Indigenous cultures, their land titles and their political representation. This variety stems largely from a common denominator: the need for protecting the \npolitical, collective rights of marginalised groups. This protection is generally intended to alleviate these groups’ economic and social disadvantages. The analysis identifies two dimensions for constitutional recognition: a wide-versus-narrow dimension and a dynamic-versus-static dimension. Both dimensions break along colonial lines, with recognition in the two postcolonial countries exhibiting a wide and static approach and recognition in the two plurinational countries exhibiting a narrow but dynamic approach. These jurisdictions could provide guidance in the Australian context, where resolving the tension between our colonial heritage and our postcolonial aspirations holds \nthe key to alleviating the disadvantages facing Indigenous Australians.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".