From Reconciliation to Reversal: Explaining Reconciliatory Backsliding in Settler Societies
Bibliographic record
Abstract
The United Nations Declaration on the Rights of Indigenous Peoples marked a pivotal moment, signalling an emerging global consensus on the recognition and protection of Indigenous peoples' inherent rights. In its wake, settler states such as Australia and New Zealand adopted a reconciliatory turn, enacting policies to advance the political, economic and cultural interests of Indigenous communities. However, recent political developments - including Australia's 2023 Aboriginal and Torres Strait Island Voice Referendum and New Zealand's 2023 General Election - have triggered a reconciliatory U-turn, with Australians rejecting pro-Aboriginal constitutional reforms and New Zealand electing a coalition government that has begun to dismantle pro-Māori policies. This article introduces the concept of 'reconciliatory backsliding' to theorize such reversals and offers an analytical framework for examining its emergence. Using New Zealand as an illustrative case study, we invite scholars to apply and refine this framework to advance research in this newly emerging and critical area.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.076 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".