Impact and Benefit Agreements and the Protection of Indigenous Peoples Rights: Any New Lessons from Canada?
Bibliographic record
Abstract
In recent times, there has been agitation for legislative recognition of indigenous land rights across different parts of the globe. There seems to be an awakening that mere reliance on the cooperation and goodwill of companies, without more, does not offer a lasting solution to calming the fear of indigenous peoples. This article, therefore, considers the proposition that indigenous groups benefit more from resource extraction with a solid legal framework. This article aligns with this proposition and argues that entrenching the rights of indigenous people as well as making an Impact and Benefit Agreement (IBA) a statutory requirement to extracting mineral resources would go a long way in further protecting the indigenous peoples rights in accordance with the United Nations Declaration on the Right of Indigenous People (UNDRIP), and other international human rights instruments. The article argues that the relative success of IBAs in Canada and some other jurisdictions notwithstanding, there is still more that can be done to ensure that IBAs not only address the ‘resource curse’ in countries blessed with mineral resources, but also become tools in empowering the indigenous peoples and protecting their rights as affirmed under relevant international legal instruments.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".