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Record W4415145592 · doi:10.14453/ltc.1720

Mining Sovereignties in Courts: Voicing Plural Sovereignties in Juridical Spaces

2025· article· en· W4415145592 on OpenAlexaboutno aff

Bibliographic record

VenueLaw/text/culture · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSovereigntyColonialismPluralOppressionState (computer science)Indigenous rights

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0240.060
Scholarly communication0.0200.017
Open science0.0020.016
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.022
GPT teacher head0.347
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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