TRANSNATIONAL JUDICIAL AND NON-JUDICIAL REMEDIES FOR CORPORATE HUMAN RIGHTS HARMS: CHALLENGES OF AND FOR LAW
Bibliographic record
Abstract
This paper will consider whether the polycentric governance approach of the 2011 United Nations Guiding Principles on Business and Human Rights has the potential to achieve the goal of transnational corporate compliance with human rights responsibilities including, importantly, the goal of access to remedy and justice for those who have been harmed. The paper was initially written as a contribution to a conference at the University of Windsor entitled Justice Beyond the State: Transnationalism and Law. First, the paper examines understandings of “citizenship” and “non-citizenship” in relation to transnational corporate [TNC] accountability in the human rights context. Two distinct perspectives are explored: first, TNC citizenship and non-citizenship and the rights and responsibilities that flow from these; and second, citizenship and non-citizenship of victims of human rights violations in relation to rights of access to remedy. Together, these insights inform an understanding of the role that transnational law and legal pluralism beyond the state could serve in facilitating remedy for human rights violations. Specifically, the paper will conclude with reflections on what might be required for implementation of the UN Guiding Principles to achieve the goal of transnational corporate compliance and access to remedy for victims of rights violations.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".