Bibliographic record
Abstract
Abstract The threats to human rights posed by non-state actors are of increasing concern. Multinational corporations, armed oppositions groups, and the activities of international organizations such as the United Nations, NATO, and the European Union are increasingly examined with recourse to a human rights lens. This book presents an approach to human rights that goes beyond the traditional focus on states and outlines the human rights obligations of non-state actors and addresses some of the ways in which they can be held legally accountable in various jurisdictions. The political debate concerning the appropriateness of expanding human rights scrutiny to non-state actors is discussed and dissected. For some extending human rights into these spheres trivializes them and allows abusive governments to distract us from ongoing violations. For others such an extension is essential if human rights are properly to address the current concerns of women and workers. The main focus of the book, however, is on the legal obligations of non-state actors. The book discusses how developments in the fields of international responsibility and international criminal law have implications for building a framework for the human rights obligations of non-state actors in international law. In turn these international developments have drawn on the changing ways in which human rights are implemented in national law. A selection of national jurisdictions, including the United States, Canada, South Africa, and the United Kingdom is examined with regard to the application of human rights law to non-state actors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".