Mining Sovereignties in Courts: Voicing Plural Sovereignties in Juridical Spaces
Bibliographic record
Abstract
This article examines how settler courts both facilitate and impede the acknowledgment of Indigenous sovereignty in socio-juridical spaces. Indigenous environmental litigation is a complex category and is characterised by a combination of factors, such as tensions between plural sovereignties and extractivism and an ambiguous relationship with the courts. This article examines two case studies as examples of Indigenous environmental litigation where courts in Australia and Canada have had an opportunity to encounter colonialism and, consequently, allude to plural sovereignties. First, the article examines two decisions from the Federal Court of Australia - Tipakalippa v National Offshore Petroleum Safety and Environmental Management Authority and Munkara v Santos NA Barossa Pty Ltd. Second, the article examines the Teal Cedar Products Ltd v Rainforest Flying Squad, decided by the Supreme Court of British Columbia. The article also engages with Povinelli's conceptualisation of ancestral catastrophe and its manifestation in the claims made by Indigenous communities in strategic environmental litigation. Through these two case studies, the article argues that the juridical openness to Indigenous knowledge and claims of plural sovereignties may provide courts with opportunities to be both epistemic allies to Indigenous peoples and a force to resist coloniality and oppression of state sovereignty.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.060 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".