Processes of Legalisation in Transnational Litigation
Bibliographic record
Abstract
Some of the most decisive battles over the responsibilities of transnational corporations (TNCs) have been fought in domestic courtrooms – often far from where the alleged abuses occurred. The United States has hosted a substantial proportion of such cases against TNCs, supported by a legal framework that historically provided several plaintiff-friendly avenues. However, the landscape has become more challenging following the Supreme Court’s decisions in Kiobel v. Royal Dutch Petroleum Co. and Daimler AG v. Bauman . In Canada, the absence of an ATS-equivalent and the application of the doctrine of forum non conveniens have limited opportunities for litigation. However, recent decisions suggest more cases may flow to Canada in the future. In the United Kingdom, developments in the law relating to parent company liability have been particularly significant. In Across continental Europe, barriers such as limited access to class actions, prosecutorial discretion, and weak disclosure obligations continue to constrain transnational human rights litigation.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".