Bibliographic record
Abstract
After more than two decades winding its way through a variety of United Nations (UN) mechanisms, in September 2007 the world’s indigenous peoples welcomed the news that the UN Declaration on the Rights of Indigenous Peoples (hereinafter the Declaration) was at last approved by the vast majority of nation-states.2 The four settler3 states that opposed the Declaration initially (the United States, Canada, Australia and New Zealand) have each in turn voiced their ultimate approval of the declaration and have issued statements in support to their indigenous citizens.In spite of the fact that these statements expressed a measure of regret for past wrongs committed, not one of those endorsements embodied a formal apology. Now that the Declaration has entered its eleventh year, many continue to question to what extent these endorsements have meaningfully advanced reconciliation for indigenous peoples and whether these endorsements were authentic in their stated desire to do more than just acknowledge the aspirations contained in the Declaration.This comment will examine the framework for political apologies in general and then consider the endorsements of the Declaration by the United States, Canada, Australia and New Zealand in light of contemporary apology theory. The article will then examine affirmative actions taken by those states following their endorsements in order to advance the claims of indigenous peoples and look at whether these actions have fallen short in providing meaningful redress for centuries of past wrongs.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| 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".