From Expropriation to Compensation: Legal Remedies for Indigenous Land Grievances
Bibliographic record
Abstract
This article aims to critically examine the legal remedies available for addressing Indigenous land grievances across various jurisdictions, focusing on restitution, compensation, recognition, and procedural mechanisms. Using a scientific narrative review approach and descriptive analysis method, this study reviewed peer-reviewed academic literature, legal texts, court rulings, and international instruments published between 2019 and 2024. Sources were selected based on relevance to Indigenous land rights and included case law from Canada, Australia, the United States, Brazil, and New Zealand. The analysis identified patterns and challenges in the legal treatment of Indigenous claims and evaluated the effectiveness of different forms of remedy. The findings reveal that while legal systems have increasingly recognized Indigenous land rights, significant limitations remain in the design and implementation of remedies. Restitution is often obstructed by evidentiary and political barriers, compensation is frequently perceived as inadequate, and legal recognition is constrained by regulatory limitations. Procedural access to justice is hindered by cost, jurisdictional fragmentation, and lack of enforcement. Across jurisdictions, legal remedies tend to reflect state-centered frameworks rather than Indigenous worldviews, leading to widespread dissatisfaction and demands for transformative reform. Although notable progress has been made in the recognition of Indigenous land rights, current legal remedies often fall short of addressing the historical and cultural dimensions of dispossession. Achieving meaningful land justice requires rethinking legal paradigms to center Indigenous epistemologies and governance systems, supported by enforceable, inclusive, and context-sensitive remedies.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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".