Building Indigenous Governance from Native Title: Moving Away from 'Fitting In' to Creating a Decolonized Space
Bibliographic record
Abstract
The business of decolonisation involves engaging with former colonial laws, policies and practices in order to create a ‘space’ for Indigenous peoples to express their unique identities, cultures and ways of knowing. In postcolonial contexts, transitional justice measures have been used as a mechanism to enable the decolonisation of legal spaces. However, decolonisation does not always guarantee a post colonial state. As a transitional justice mechanism, native title in Australia has evolved via the common law to recognise the relationships that Indigenous peoples have with their land and waters. However, native title has been accused of limiting the ability of native title holders to engage effectively in governance structures. Drawing on parallels in the Canadian context, we consider the limitations of native title law as a tool for decolonisation and the constraints imposed by Australia’s federal constitutional structure. The paper then outlines the legal regime established under native title discussing how it operates outside the realm of ‘government’. We then consider the way in which native title holders engage with Indigenous and non-indigenous governance within this ‘private sector’ before discussing whether native title has been able to provide a decolonised space within Australia’s governance system.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".