Human Rights Litigation against Multinationals in Practice
Bibliographic record
Abstract
This book reviews the current position in this field, which has developed over the past 25 years, designed to hold multinationals to account, legally, for human rights abuses in the Global South. The authors are practising lawyers who have litigated and led prominent cases of legal significance in this field. Although the focus is on the Global North, where most of the cases have been brought—United Kingdom, United States, Canada, Australia, France, Netherlands, and Germany—there is also a chapter on South Africa. The cases cited include claims against parent companies for harm caused by subsidiary operations, claims for corporate complicity in violations perpetrated by States, and claims arising in a supply chain context. Whilst other books have included consideration of the legal aspects of many of the cases, the focus here is on the interrelated strategic and practical, as well as legal, considerations on which viability and prospects of success depend. In addition to questions of jurisdiction, applicable law, and theories of liability, obstacles to justice concerning issues such as access to information, collective actions, witness protection, damages and costs, and funding regimes (including a specific chapter on litigation funding), and issues relating to public pressure and settlement, are discussed. Although most of the authors act for victims, there is a substantial chapter providing the perspectives of business. Since this area of litigation has developed concurrently with, and has formed part of, the rapidly mushrooming field of business and human rights, the contextual relevance of the UNGPs is considered.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".