Stemming the Flow of Corporate Human Rights Abuses: Incorporating the Ruggie Report in the Common Law Doctrine of Foreign Judgment Enforcement
Bibliographic record
Abstract
The eminent case of Aguinda v Chevron Corporation, currently in its twentieth year of litigation, represents a growing phenomenon in international commercial litigation between multinational corporations and victims of human rights abuse from developing nations. In 2011 Aguinda awarded approximately US$18 billion against Chevron for extreme environmental and human rights abuse from oil contamination in the Amazon region of Ecuador. Chevron has removed its assets from Ecuador’s jurisdiction leaving the plaintiffs without remedy. This paper traces Aguinda to Canada where the plaintiffs’ action in Yaiguaje to enforce the judgment to satisfy their debt is stayed. This paper critiques this decision of the Ontario Superior Court of Justice as being unprincipled and failing to consider the wider implications of its decision on the struggle for developing nations to remedy human rights abuses by multinational corporations. This paper argues that the common law doctrine of foreign judgment enforcement must evolve to reflect the needs of modern society. The paper does this by incorporating the “Protect, Respect and Remedy: A Framework for Business and Human Rights” report released by the United Nations in 2011.
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 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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".