Bibliographic record
Abstract
This paper examines the role of domestic courts as potential fora for the resolution of international law disputes. It starts with a review of the 2020 decision of the Supreme Court of Canada in Nevsun Resources v. Araya , in which the Court cracked open the door to plaintiffs pursuing civil remedies against a parent company for alleged violations of human rights and other international law arising from the conduct of a subsidiary abroad. It then examines the Nevsun case from a comparative perspective, focussing on where it fits analytically in the continuum of litigation under the Alien Tort Statute in the United States and recent developments in litigation in the United Kingdom and the Netherlands against parent companies for the conduct of their subsidiaries where human rights and international law feature to varying degrees in the claims alleged. It concludes by noting that while litigation in the space is likely to increase hand in hand with the focus on environmental, social and governance (ESG) issues generally, at the moment there is no consistency in how such litigation is being adopted by and addressed by domestic courts. This divergence across jurisdictions creates a host of challenges and opportunities for disputes at the intersection of corporate liability and international legal norms.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".