Informal instruments to impose human rights obligations on foreign investors: An emerging practice of legality?
Bibliographic record
Abstract
Abstract In parallel to the negotiation of international investment agreements to protect foreign investment, intergovernmental organizations have deployed considerable efforts to adopt and implement standards of conduct for business enterprises operating abroad. Despite their informal character under international law, these instruments are increasingly mentioned in international investment agreements and investment arbitration. How can references to informal instruments elaborated by intergovernmental organizations contribute to the imposition of human rights obligations on foreign investors in international investment law? Drawing upon the interactional theory developed by Jutta Brunnée and Stephen J. Toope, this article considers these references as a practice that has the potential to strengthen the normative pull towards compliance with human rights norms. In addition to emphasizing the role of international investment law as a relevant forum to develop a practice surrounding these informal instruments, it assesses whether the use of these instruments by members of a community of practice is intended to establish a genuine sense of obligation and to impose human rights obligations on foreign investors. Even if some instances evidence a practice that strengthens such a sense of obligation, most of the references included in international investment agreements and investment arbitration do not render a practice of legality.
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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 | 0.042 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.084 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".