ARAYA V. NEVSUN RESOURCES: REMEDIES FOR VICTIMS OF HUMAN RIGHTS VIOLATIONS COMMITTED BY CANADIAN MINING COMPANIES ABROAD
Bibliographic record
Abstract
In November 2017, the British Columbia Court of Appeal (BCCA) published its decision in the case Araya v. Nevsun Resources , dismissing the appeal filed by Nevsun, and allowing the lawsuit to move forward to the merits stage of the procedure. This decision was ground-breaking since the plaintiffs were suing Nevsun Resources, a Canadian mining company, for its alleged complicity in the use of forced labor, slavery, torture, inhuman or degrading treatment, and crimes against humanity at the Bisha Mine in Eritrea, a mine belonging to Nevsun. In its decision, the BCCA rejected the three main arguments put forward by Nevsun to get the case dismissed: (1) the forum non conveniens doctrine; (2) the Act of State doctrine and (3) the lack of private law cause of action against corporations for the violations of customary international law principles. In this context, this article offers an analysis of the most significant cases brought before Canadian Courts in regard to Canadian mining companies’ corporate social responsibility. It also relies on two influential cases from the U.S. Supreme Court: Kiobel v. Royal Dutch Petroleum and Jesner v. Arab Bank. Finally, it looks at the common challenges faced by foreign victims when they seek to bring lawsuits against transnational corporations and it briefly suggests that common law courts should adopt a new duty of care to address businesses’ corporate liability for violations of human rights.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".