Uninvited guests: NGOs, «amicus curiae briefs», and the environment in the international investment regime
Bibliographic record
Abstract
Environmental regulation is often at the heart of investment treaty arbitration. As the international investment regime, bolstered by the powerful process of investor-state dispute settlement (ISDS), continues to expand into and gain influence in distant corners of domestic environment governance, the pressure points created where the investment regime comes into conflict with environmental regulation have also multiplied. These collisions between the investment regime and the environment have generated public concern. However, the regime has remained seemingly unreceptive to these concerns and has hesitated to embrace participation by outside actors—such as non-governmental organizations (NGOs)—who would see such concerns accounted for in ISDS. This study explores this complicated nexus between the investment regime, the environment, and NGOs. More specifically, it lays bare the relationship between the investment regime and its environmental "others," focusing on the role of NGOs, through the use of amicus curiae briefs in ISDS, in both shaping (and compelling) these interactions and encouraging greater openness and responsiveness in the regime to environmental issues. Ultimately, a discursive analysis of the NGO amicus curiae briefs submitted in environment-investment disputes and the responses to them from the regime's decision makers reveals that through this practice, NGOs have a crucial role in transforming the regime. However, this potential is currently underdeveloped and hampered by the very closed-off nature of the regime that these actors seek to overcome.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".