Indigenous peoples’ human rights, self-determination and local governance – Part 1
Bibliographic record
Abstract
This is the first of two articles exploring the international human rights framework as it relates to Indigenous peoples’ land rights and interests, with a focus on Australia. Over the past 30 years, the international community has increasingly recognised that special attention needs to be paid to the individual and collective rights of Indigenous peoples, as they are among the world’s most marginalised peoples. For a long time, the Indigenous peoples of the world have used the international human rights system to tackle discrimination and abuses of their rights, and the United Nations has increasingly become a place for them to voice their concerns. In Australia, there has been a long-running debate about the lack of recognition of the First Peoples in Australia’s Constitution. Aboriginal and Torres Strait Islander peoples are increasingly demanding that the full suite of international human rights norms and standards are applicable to their affairs and to dealings with them, including the UN Declaration on the Rights of Indigenous Peoples. This first article discusses the international human rights framework as it relates to the Indigenous peoples of Australia. The second article will take a closer look at how the land rights and interests of the Aboriginal and Torres Strait Islander peoples are being recognised at the national and state jurisdictional levels within Australia, with reference to recent comparable actions in Canada and New Zealand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".