What's the Matter with CANZUS? Understanding Canada, Australia, New Zealand, and the United States' UNDRIP Reversal
Bibliographic record
Abstract
This research investigates the puzzling shift in stance by the CANZUS states (Canada, Australia, New Zealand, and the United States) regarding the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP). Initially, in 2007, these liberal democracies voted against the declaration, citing concerns over sovereignty and self-determination principles. However, within a decade, all four countries had reversed their position and endorsed UNDRIP, despite no changes to the text. This study seeks to understand the factors that led to this reversal and how these states justified their shift on the global stage. Utilizing Putnam's two-level game theory and global fields theory, the research proposes that the convergence of international norms with domestic factors, rather than either alone, drove the eventual endorsement of UNDRIP. Process tracing reveals that domestic political shifts, activism, and changes in coalition politics were pivotal in aligning domestic win-sets with international expectations. Moreover, this study highlights the rhetorical adaptation techniques employed by CANZUS states to localize the meaning of the declaration, thereby reconciling international commitments with domestic realities. Through an analysis of speeches, formerly confidential communications, and drafts of UNDRIP from Australia and Canada, the research uncovers the strategic efforts by these states to symbolically support Indigenous rights while navigating the tension between sovereignty and self-determination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".