Human Rights, Transnational Corporations, and Embedded Liberalism: What Chance Consensus?
Bibliographic record
Abstract
This paper contextualises current debates over human rights and transnational corporations. More specifically, we begin by first providing the background to John Ruggie’s appointment as ‘Special Representative of the Secretary-General on the issue of human rights and transnational corporations and other business enterprises’. Second, we provide a brief discussion of the rise of transnational corporations, and of their growing importance in terms of global governance. Third, we introduce the notion of human rights, and note some difficulties associated therewith. Fourth, we refer to Ruggie’s scholarly work on ‘embedded liberalism’, the ‘global public domain’, and ‘social constructivism’. Following this, we refer to the other five papers contained in this Journal of Business Ethics special issue, ‘Spheres of Influence/Spheres of Responsibility: Multinational Corporations and Human Rights’, and consider some of the potential obstacles to Ruggie’s recent suggestion that a ‘new consensus’ has formed, or is forming, around his ‘Protect, Respect and Remedy’ framework. We conclude by raising questions regarding the processes of consensus-building around, and the operationalisation of, Ruggie’s ‘Protect, Respect and Remedy’ framework.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".