Foreign Judgments, Judicial Trailblazing and the Cost of Cross-Border Complexity: Thoughts on Chevron Corp v Yaiguaje
Bibliographic record
Abstract
This commentary examines the decision in Chevron Corp v Yaiguaje, which was released by the Supreme Court of Canada (SCC) on 4 September 2015. In the case, the SCC considered whether a Canadian court had jurisdiction to recognise and enforce a US$9.51bn judgment against Chevron (and its wholly owned subsidiary), which was issued by an Ecuadorian court. The SCC held that it did. It reasoned that there was no requirement for a real and substantial connection between a foreign judgment debtor (or the dispute from which the judgment arose) and a Canadian court before such a court has jurisdiction to recognise and enforce a foreign judgment. As a result, it appears that Canadian courts may be applying the low domestic standard for recognising awards from another province to awards from foreign countries. The fallout from this case will prove to be divisive. Human rights advocates will celebrate this case, hoping that it signals that Canadian courts will be taking a more active role in holding extraction companies accountable for human rights violations and environmental damages abroad. Sceptics will fear this case, believing that Canada’s new role as a judicial trailblazer will come at a cost, discouraging foreign investment and potentially undermining international relations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".